In an era where data drives strategic decision-making, data governance and the quality of that data become increasingly vital.

Building a business case for data governance can bring a number of enterprise-wide benefits. This is especially true in banking and financial services, where the risk-focused mindset can sometimes overshadow the potential to become data-driven.

However, it is often a challenge to communicate the value of investing in a data governance and analytics programme.

Successful data governance programmes are influenced by more than the deployment of advanced technologies or methodologies. They are also determined by fostering an organisational culture that fundamentally prioritises data governance. Often referred to as this focus on encouraging a data-driven culture helps ensure that better data management efforts are adopted and sustained over time. Consequently, this can lead to improved data quality, better adherence to rules, and smarter decision-making across the company.

In a recent roundtable in London with some of our customers, we gained first-hand insight into how they are tackling the challenge of fostering a company culture that values data governance. As thought leaders in their fields, we thought we’d share some of their insights. We’ve broken these tips down into simple summaries below.

The Four Essential Tips

Here are four ways that our customers cultivate a company culture that prioritises data governance:

1. Start with Data Quality
2. Highlight Success Stories
3. Use Positive Language
4. Tap into the Human Side of Data Governance

1. Start with Data Quality

Our customers agreed that this is one of the most impactful steps. Data quality is the foundation, ensuring consistency and accuracy across the organisation鈥檚 data landscape. This is essential for any governance and analytics programme to succeed. This step helps make the benefits of data governance more apparent and relatable to all employees, as stakeholders see how data quality can enhance decision-making, reduce errors, and streamline processes. Equally, better data powers better decisions, more of which to follow…

2. Highlight Success Stories

When trying to gain buy-in internally, it’s important to be able to create a compelling story that your key stakeholders can relate to. Success can look different depending on every organisation and it’s particularly important to shout about the wins, big or small. For one organisation, having proper data governance can drive efficiencies and profits. For another, it could result in more lives saved. Real-life examples of how improved data governance has led to better outcomes can be an excellent motivator for change.

3. Use Positive Language

The way data analytics and governance are talked about has the power to significantly influence key stakeholders. This can be as simple as talking about the opportunities and benefits of having a robust data governance programme, instead of framing it as something that’s necessary to comply with regulations. Compliance is critical, but so is growing your business; consequently, demonstrate the value your improved data quality is bringing in clear dashboards.

4. Tap into the Human Side of Data Governance

While it may be true that people will frequently resist change, it doesn’t have to derail your ambitions. To deal with this effectively, try to identify some of the areas of frustration felt by other teams across the organisation. To begin with, ask them about their daily work challenges. Oftentimes, these challenges are caused by underlying problems with data quality. Understanding this helps convince them of the value of investing more in data governance to make their day-to-day jobs easier. Our customers also commented on the value of having good interpersonal skills to work effectively with stakeholders and deal with push-back.

Maintaining a Successful Data Governance Programme

Once these initial steps have been taken, continue the conversation through ongoing education and training. Offering workshops, seminars, and online courses can help demystify data governance and analytics, making it more accessible across the business.

Another way to sustain an enterprise data governance programme is by leveraging technology. User-friendly, no-code tools and platforms are a great way of democratising data governance, making it more accessible across the business. With AI, these tools can automate mundane tasks, extract valuable insights from the data, and ensure data accuracy. Accordingly, this makes it easier to encourage a company-wide culture that values data governance.

Conclusion

Fostering a company culture that values data governance is a multifaceted process. With this in mind, it’s worth seeing how our customers have gone about it. In general they achieve buy-in by starting with data quality; leveraging the power of storytelling; providing continuous education; and embracing data management technologies. By focusing on these areas, organisations can ensure that their data governance efforts move beyond compliance requirements to become strategic advantages driveing better decision-making and operational efficiency.

How 糖心传媒 can help

Looking for advice on how to build a business case for data governance within your organisation? This is something we’ve done for our clients.

We have developed 糖心传媒 Catalyst, our professional services offering, to deliver practical support in your data strategy. 

From augmenting your data team to working on specific data projects, delivering training or providing a short-term specialist to solve a specific data quality problem, let 糖心传媒 Catalyst accelerate your ambitions, help you increase data literacy and foster a data-driven culture.

Have a look at our Catalyst page to find out more: www.datactics.com/

The post Four Essential Tips to Build a Data Governance Business Case appeared first on 糖心传媒.

We are currently in an exciting area and time, where Machine Learning (ML) is applied across sectors from self driving cars to personalised medicine. Although ML models have been around for a while – for example, the use of algorithmic trading models from the 80鈥檚, Bayes since 1700s – we are still in the nascent stages of productionising ML.

From a technical viewpoint, this is ‘Machine Learning Ops’ or MLOPs. MLOPs involve figuring out how to build, deploy via continuous integration and deployment, tracking and monitoring models and data in production.听

From a human, risk, and regulatory viewpoint we are grappling with big questions about ethical AI (Artificial Intelligence) systems and where and how they should be used. Areas including risk, privacy and security of data, accountability, fairness, adversarial AI, and what this means, all come into play in this topic. Additionally, the debate over supervised machine learning, semi-supervised learning, and unsupervised machine learning, brings further complexity to the mix.

Much of the focus is on the models themselves, such as听听Everyone can get their hands on pre-trained models or licensed APIs; What differentiates a good deployment is the data quality.

However, the one common theme that underpins all this work, is the rigour required in developing production-level systems and especially the data necessary to ensure they are reliable, accurate, and trustworthy. This is especially important for ML systems; the role that data and processes play; and the impact of poor-quality data on ML algorithms and learning models in the real world.

Data as a common theme听

If we shift our gaze from the model side to the data side, including:

• Data management – what processes do I have to manage data end to end, especially generating accurate training data?
• Data integrity – how am I ensuring I have high-quality data throughout?
• Data cleansing and improvement – what am I doing to prevent bad data from reaching data scientists?
• Dataset labeling – how am I avoiding the risk of unlabeled data?
• Data preparation – what steps am I taking to ensure my data is data science-ready?

A far greater understanding of performance and model impact (consequences) could be achieved. However, this is often viewed as less glamorous or exciting work and, as such, is often unvalued. For example, what is the impetus for companies or individuals to invest at this level (such as regulatory 鈥� e.g. BCBS, financial, reputational, law)?

Yet, as well defined in

鈥淒ata largely determines performance, fairness, robustness, safety, and scalability of AI systems鈥yet]听In practice, most organizations fail to create or meet any data quality standards, from under-valuing data work vis-a-vis model development.鈥澨�

This has a direct impact on people’s lives and society, where 鈥�…data quality carries an elevated significance in high-stakes AI due to its heightened downstream impact, impacting predictions like cancer detection, wildlife poaching, and loan allocations鈥�.

What this looks like in practice

We have seen this in the past, with the in the UK during Covid. In this case, teachers predicted the grades of their students, then an algorithm was applied to these predictions to downgrade any potential grade inflation by the Office of Qualifications and Examinations Regulation, using an algorithm. This algorithm was quite complex and non-transparent in the first instance. When the results were released, 39% of grades were downgraded. The algorithm captured the distribution of grades from previous years, the predicted distribution of grades for past students, and then the current year.

In practice, this meant that if you were a candidate who had performed well at GCSE, but attended a historically poor performing school, then it was challenging to achieve a top grade. Teachers had to rank their students in the class, resulting in a relative ranking system that could not equate to absolute performance. It meant that even if you were predicted a B, were ranked at fifteenth out of 30 in your class, and the pupil ranked at fifteenth the last three years received a C, you would likely get a C.

The application of this algorithm caused an uproar. Not least because schools with small class sizes – usually private, or fee-paying schools – were exempt from the algorithm resulting in the use of the teaching predicted grades. Additionally, it baked in past socioeconomic biases, benefitting underperforming students in affluent (and previously high-scoring) areas while suppressing the capabilities of high-performing students in lower-income regions.

A major lesson to learn from this, therefore, was transparency in the process and the data that was used.

An example from healthcare

Within the world of healthcare, it had an impact on ML cancer prediction with IBM鈥檚 ‘Watson for Oncology’, partnering with The University of Texas MD Anderson Cancer Center in 2013 to 鈥渦ncover valuable insights from the cancer center鈥檚 rich patient and research databases鈥�. The system was trained on a small number of hypothetical cancer patients, rather than real patient data. This resulted in erroneous and dangerous cancer treatment advice.

Significant questions that must be asked include:

• Where did it go wrong here 鈥� certainly the data but in general a wider AI system?
• Where was the risk assessment?
• What testing was performed?
• Where did responsibility and accountability reside?

Machine Learning practitioners know well the statistic that 80% of ML work is data preparation. Why then don鈥檛 we focus on this 80% effort and deploy a more systematic approach to ensure data quality is embedded in our systems, and considered important work to be performed by an ML team?

This is a view recently articulated by who urges the ML community to be more data-centric and less model-centric. In fact, Andrew was able to demonstrate this using a steel sheets defect detection prediction use case whereby a deep learning computer vision model achieved a baseline performance of 76.2% accuracy. By addressing inconsistencies in the training dataset and correcting noisy or conflicting dataset labels, the classification performance reached 93.1%. Interestingly and compellingly from the perspective of this blog post, minimal performance gains were achieved addressing the model side alone.

Our view is, if data quality is a key limiting factor in ML performance 鈥搕hen let鈥檚 focus our efforts here on improving data quality, and can ML be deployed to address this? This is the central theme of the work the ML team at 糖心传媒 undertakes. Our focus is automating the manual, repetitive (often referred to as boring!) business processes of DQ and matching tasks, while embedding subject matter expertise into the process. To do this, most of our solutions employ a human-in-the-loop approach where we capture human decisions and expertise and use this to inform and re-train our models. Having this human expertise is essential in guiding the process and providing context improving the data and the data quality process. We are keen to free up clients from manual mundane tasks and instead use their expertise on tricky cases with simpler agree/disagree options.

To learn more about an AI-driven approach to Data Quality, read our press release about our Augmented Data Quality platform here.听

The post The Importance of Data Quality in Machine Learning appeared first on 糖心传媒.

Unlocking data value for business and compliance insight

Data Management Summit (DMS) London听is now in its 13^th year and takes place听in person听on听30^th March听补迟听America Square Conference Centre听颈苍听London, to explore how data strategy is evolving to drive business outcomes and speed to market in changing times.

As financial institutions strive to meet digital transformation goals and drive closer alignment with the business, keeping up with the latest data management innovations and adoption of cloud, AI and ML technologies that require new approaches and capabilities, is critical for driving innovation and a competitive edge.

Join the leading financial data management conference for sell side and buy side executives in capital markets, to hear from data practitioners and innovators who will share insights into how they are pushing the boundaries with data to deliver value with flexible but resilient data driven strategies.

Topics will include:

• How to achieve successful data transformation for long term business value
• How to capitalise on data migration to the cloud
• How to enable data democratisation with trusted data products (featuring 糖心传媒’ Head of Product, Luca Rovesti)
• Managing UK-EU regulatory divergence and meeting requirements across multiple regulatory regimes 鈥� the data management response
• The power of automation 鈥� how to听 leverage automation and ML to progress your data analytics capability and get value from unstructured data
• Implementing data fabric for business value creation
• How to set up a robust ESG data framework for compliance and business insight

The post Data Management Summit London | 30/03/2023 appeared first on 糖心传媒.

糖心传媒 has won 鈥楤est Data Quality Analysis Tool鈥� at A-Team Group鈥檚 Data Management Insight Awards 2021. 

For the third year running, 糖心传媒 has been recognised for its business-user focused technology at the A-Team awards, which focuses on leading providers of data management solutions, services and consultancy to capital markets participants. Nominees for the awards were voted for by clients and financial users in the data management space, with the winners decided by the A-Team鈥檚 Editorial Team and Advisory Board. The notable awards, now in their ninth year, celebrate industry-leading solutions across a number of data management categories, from best sell-side and buy-side enterprise data management platforms, to data governance, data quality, and KYC on-boarding solutions.  

糖心传媒 CEO said, 

鈥淲inning the award for Best Data Quality Analysis Tool is extremely valuable to us, as we continue to innovate and develop our data quality solutions to meet the demands of clients across financial services.  

鈥淲e鈥檝e seen significant growth within the Data Management market in the last year, with more organisations wanting to modernise their data infrastructure and embrace data quality solutions. This is where 糖心传媒 adds value to organisations, as our Self-Service Data Quality platform empowers subject matter experts to measure and fix their data themselves, without having to rely on IT departments.  

鈥淗aving recently made it into Gartner鈥檚 Magic Quadrant for the first time, we鈥檙e delighted to be further recognised for our Self-Service Data Quality platform by the A-Team and would like to thank all those who voted for us.鈥� 

Angela Wilbraham, CEO of A-Team Group, which hosts the Data Management Insight Awards said, 

鈥淚t has been fantastic to see such a high calibre of entries in our Data Management Insight Awards 2021. There are some really deserving winners and we congratulate 糖心传媒 on winning Best Data Quality Analysis Tool and for their contribution to the financial data management industry.鈥� 

糖心传媒 糖心传媒: 

糖心传媒 provides business user-focused, no-code鈥�data quality鈥痑nd matching tools helping financial firms gain value from their data and reduce regulatory risk. Its award-winning platform integrates with multiple data sources, governance, and lineage systems with intelligent automation. It allows Chief Data Officers and senior data leaders to measure, report and fix their data, and match across multiple internal and external sources and systems. Using AI, the platform significantly reduces the manual effort required to make decisions, with full transparency. For more information or to book a demo please鈥�contact us.听

For more information on how 糖心传媒鈥� Self-Service Data Quality platform can add value to your business please contact 

And for more from 糖心传媒, find us on ,  or 

The post 糖心传媒 wins 鈥楤est Data Quality Analysis Tool鈥� at DMI Awards 2021 appeared first on 糖心传媒.

Many businesses find that the data they collect has limited reliability.鈥♀�痳ecent鈥�痓y Experian highlighted 鈥痶hat鈥�55% of business leaders do not trust their data, ultimately impacting the confidence that business leaders have over the data they collect.鈥�&苍产蝉辫;

Bad data can have significant business consequences鈥痜or companies.鈥疪esearch by鈥� found that approximately 77% of companies鈥痟ave the belief that their bottom line is affected by inaccurate and incomplete data, with a further 12%鈥痮f revenue being believed to be wasted due to poor data quality. Nevertheless, companies that did put a focus on high-quality data saw a revenue increase of 15% to 20%.鈥�&苍产蝉辫;

So let鈥檚 unpack in more detail those reasons why Data Quality is important: 

1) Business Decision Making鈥�鈥�&苍产蝉辫;

The usage of a data quality vendor ensures ongoing data quality checks, which ultimately make sure that enterprises have cleaner, safer, and high-quality data. This offers organisations more accurate analytics, clearer insights, and predictive advantages.鈥疧verall,鈥痩eaders can have greater confidence in their business decision making.鈥�&苍产蝉辫;

2) Helps with scalability鈥�鈥�&苍产蝉辫;

Firms鈥痑re able to鈥痵cale more quickly with a strategic鈥痑nd effective data quality model in place. If infrastructure operates automatically, it also becomes a possibility to scale up without the need to increase manpower.鈥�&苍产蝉辫;

3) Operational Efficiency and Productivity鈥�鈥�&苍产蝉辫;

A data quality tool鈥痯rovides an effective way of removing the need for firms to manually verify large volumes of data. This is often resource heavy, laborious and can lead to duplicate investigations. High quality data overall helps to reduce mistakes鈥痺ith less time needed to manually fix inconsistencies.鈥�&苍产蝉辫;

4) Regulatory Compliance鈥�&苍产蝉辫;

Regulators demand high quality, accurate data specifically measured for a vast array of regulations 鈥� including BCBS 239, a data-driven regulation in and of itself. Businesses need a technology framework that enables rapid delivery of鈥�data quality鈥�measurement and remediation鈥痶o ensure ongoing compliance. It also needs to save time and effort and鈥痯rovide results to senior management that can demonstrate compliance with regulations driven by robust鈥� data quality management.鈥�&苍产蝉辫;

For organisations that have cultural problems around data, how best can they address these?鈥�鈥� 

A lot of people associate owning data or being a data steward with being鈥痳esponsible or鈥痟eld to account in a negative way. As the banking and financial services sector is underpinned by risk management, a common challenge is overcoming people鈥檚 fears around taking responsibility for data. 

础鈥�culture shift鈥痭eeds to happen to ensure that people鈥痜ocus on better鈥痙elivery,鈥痳ather than being afraid of responsibility.听We asked Head of Sales at 糖心传媒, to comment on this further.听

鈥淥ne of our听London-based听wealth management clients鈥痟as gone about this鈥痠n鈥痶he best way,鈥痓ecause they have a senior leadership team that really understands the criticality of data to the business. They understand鈥痠t is鈥痑bout听别尘辫辞飞别谤颈苍驳听those鈥痳esponsible to invest in the quality of data.鈥澨�

This could mean, for example, being able to鈥痵ay鈥�淚f鈥痶he鈥痙ata鈥痺as 30% better on client information, I would expect to see a 5% increase in revenue鈥痮r in鈥痬argin.鈥濃�疞inking the information to business outcomes can help people understand the importance of driving up the quality of information relied鈥痷pon,鈥痑nd how it can help to drive a long-term revenue strategy.鈥���&苍产蝉辫;

Many of the businesses being built today that鈥痙on鈥檛鈥痟ave鈥�榖ad data debt鈥欌�痑re in a great position to benefit from modern techniques from the word go-鈥痶his鈥痵tarting point is what many banks鈥痑spire鈥痶o鈥痳eplicate. It can be said that if you鈥痙on鈥檛鈥痑ddress cultural issues, there is a risk that the business will end up totally disintermediated and losing what made them great to begin with.鈥�&苍产蝉辫;

To have further conversations about why Data Quality is important to an organisation, reach out to 

The post Data Quality is important to an organisation for many reasons appeared first on 糖心传媒.

Yesterday, 28^th April, Kieran Seaward, Head of Sales took part in a panel discussion entitled 鈥楨stablishing a strong data management foundation for trusted and connected data to drive science and AI鈥�. He spoke alongside Andrea Smith, BNY Mellon; Stan 翱鈥横补谤谤补, Invesco US; Malavika Solanki, The Derivatives Service Bureau, and Robert Wallos, West Highland. The panel was then moderated by Niresh Rajah. 

The panel gave the contributors the opportunity to explore: 

– The role of data lineage and governance in sourcing, mapping, and managing alternative and unstructured data. 
– How to apply AI/ML and data discovery tools to help monitor, automate, and discover new data assets, enrich the existing data foundation, connect data silos, manage privacy risk. 
– How to ensure high quality and trusted data across the organisation. 
– How to pull these disciplines together to drive data science and AI/ML and deliver business results. 

We听caught up听with Kieran about the panel and delved into听the critical topics听of听building confidence in the quality of the data,听data management and the challenges of unstructured and alternative data.听

To kick off, Kieran, business users are reliant on high quality data in order to generate insights. How can we trust the quality of our data and how are you building confidence in the quality of your data? 

A number of our client firms have implemented what we term a 鈥楧ata Quality Firewall鈥� to ensure the integrity of information at ingestion stage, typically from regional or divisional offices, from external partner providers or from vendors. 

Regarding ‘building trust or confidence’, I would say measure it from the bottom up using industry standard dimensions of data quality, and in doing so involve data stewards and subject matter experts from the various business lines for two reasons: 

1) They will provide the business context and understanding of the data to help define the rules to measure the data; 

2) They will also come along as part of the journey and fully understand the reasons for doing this, making it easier to help them 鈥榖uy-in鈥� to the process. 

Once that accurate measurement is in place, you can provide data stewards and business users with a live picture of the quality of their data via data quality dashboards 鈥� again building confidence. You can go one further than just reporting on data and empower these users to remediate / correct the data for which they are responsible. 

This all takes time, but it does work in building confidence in the data at all levels of the organisation. 

What are the challenges of unstructured and alternative data, and how can they be integrated into data platforms and used beneficially by the business? 

Firms in the alternative investment space such as Private Equity and Hedge Funds absolutely rely on alternative data as the basis of some of their research. They need to enrich their standardised internal data with alternative data sources. As an example 鈥� getting an accurate picture of unlisted firms by utilising company registration office data, such as UK Companies House, or vendor sources such as Pitchbook / Crunchbase. No common identifier exists so you have to map or match that data by different means. This is still proving a major challenge for these firms. In our experience, a centralised approach to standardisation rather than Data Scientists in different areas of the business doing their own thing enables / ensures a harmonised view across the enterprise. 

Of course, the accuracy of alternative or unstructured data can be questionable. Anyone who has worked with UK Companies House data will testify to that! However, the benefits of the insights you can generate from these sources is worth overcoming the technical and reliability challenges associated with integrating them with your internal data / systems. Unified, connected, harmonised data is the panacea. 

To have further conversations about building confidence in the quality of your data and understanding fully the challenges of unstructured and alternative data, reach out to , Head of Sales. If you wish to find out more about how 糖心传媒 is using Artificial Intelligence to achieve faster and more accurate analytics, you can reach out directly to Dr , Head of Artificial Intelligence.  

Click听for more on Data Management, or find us on听,听听or听听for the latest news from 糖心传媒.听听

The post ‘Establishing a strong data management foundation for trusted and connected data to drive data science and AI’ | An interview with Kieran Seaward appeared first on 糖心传媒.

Welcome to our fortnightly round-up from 糖心传媒, highlighting some great moments of achievement for us… 

Bloor Mutable Award 2021 

Just like the blink of an eye, Friday is upon us again and it鈥檚 time for us to tell you all the exciting developments that have been happening over the past two weeks! 

To kick-off, we delightedly announced this week that 糖心传媒 has secured a further accolade for the quality of its technology, this time from Bloor Research. 糖心传媒 Self-Service Data Quality (SSDQ) has been awarded the Platinum Mutable Award in the 2021 Report.

The award recognises software vendors that enable their clients to deal with data management processes in an intelligent and business-focused way. 

Download your copy of the InBrief article from Bloor Research to find out more. 

糖心传媒 is a leader in the 2021 SoftwareReviews Data Quality Data Quadrant 

We are thrilled to announce that 糖心传媒 has been recognised as a leader in the 2021 Data Quality Data Quadrant report from SoftReviews (a division of IT research and consulting from Info-Tech Research Group). 

The award is based on the collective experience of real users, and placement is based on their satisfaction with the product features, vendor experience, capabilities and emotional sentiment. 糖心传媒 was named a leader, with a composite score of 8.5, which represents the complete and aggregated satisfaction score from end users. Vendor support and availability & quality of training were two of the strongest capabilities associated with 糖心传媒, resulting in a 100% Plan to Renew reported by its users.  

We managed to also secure the top satisfaction scores in a variety of areas representing vendor capabilities and product features, including: Satisfaction that Cost is Fair Relative to Value, Ease of IT Administration and Data Matching & Data Cleansing.  

The Finnovate Show featuring Matt Flenley available to listen to! 

Matt Flenley, recently contributed to The Finnovate Show, a podcast that gives a space for financial service leaders that are leading change and innovation a chance to share. 

The podcast covered topics including: 

• How听糖心传媒听has evolved during COVID-19听
• How the current regulatory environment is evolving听
• The implications of how the current regulatory environment is evolving听
• Where the current regulatory environment leaves FIs.听

Matt鈥檚 podcast can be listened to

Contributed to Nigma Community Meet up 

On the 31st March, our Head of Artificial Intelligence, Dr Fiona Browne shared at the  event on the topic of 鈥楬ow to get started with AI and ML鈥�. This is a Meetup which is aimed at keeping student developers and learners up-to-date on the latest technology stacks and current software trends that are being demanded by employers. It can be difficult to be a student or learner in the software sector, heading out into the industry for the first time, whether that be on your placement, internship or your first graduate job. Jonathan Armstrong, Cirdan and Darren Broderick from Liberty IT also contributed to the event. Overall, the feedback was brilliant with lots of great questions asked. We are delighted to be able to support the next wave of developers. 

We attended FinTech Talents North America 2021 

Last week, we attended a delegation of 12 other Northern Irish companies for a virtual conference entitled 鈥楩inTech Talents North America鈥�.

The event showcased a selection of leading financial services companies from Northern Ireland who offer a range of innovative services and solutions for areas including FinTech, Cybersecurity and Data Analytics, AI and IOT, servicing banks, corporations, governments, and utilities worldwide. The event was highly valuable with great conversations happening throughout. We were thrilled to be a part of it! 

Welcome to Roisin and Jack! 

We are excited that our team has continued to grow. We have welcomed Roisin Floyd into the Marketing Team and Jack Torrens into the pre-sales team. We interviewed both of them about their new venture. You can read both pieces here by clicking the following images. 

Upcoming Events 

Stuart Harvey is contributing to a webinar on the topic of ‘饾棝饾椉饾槃 饾榿饾椉 饾棽饾榾饾榿饾棶饾棷饾椆饾椂饾榾饾椀 饾棻饾棶饾榿饾棶 饾椌饾槀饾棶饾椆饾椂饾榿饾槅 饾棶饾椈饾棻 饾棻饾棶饾榿饾棶 饾棿饾椉饾槂饾棽饾椏饾椈饾棶饾椈饾棸饾棽 饾棾饾椉饾椏 饾棶饾椈饾棶饾椆饾槅饾榿饾椂饾棸饾榾’, brought to you by . 

Register for the webinar to find out: 
 

• How to establish data quality听
• How to implement effective data governance听
• The benefits of combining data quality and governance听
• The best technologies and tools for these tasks听
• How to ensure accurate and meaningful analytics听

To register for this event on the 22nd of April, click  

Finally, we hope you have a Happy Easter, from all of us at 糖心传媒! 

For more on AI and Ethics, Data Quality, or just an introduction to our new team members, you can find us on ,  or . 

The post 糖心传媒 Round-Up: Bloor Research Platinum Award & The SoftwareReviews Data Quality Quadrant appeared first on 糖心传媒.

A Finnovate Show Appearance 

Just like that it鈥檚 Friday again and it鈥檚 time for us to update you on the excitement that鈥檚 been happening at 糖心传媒 over the past fortnight.  recently spoke on the Finnovate Show, a podcast that gives a space for financial service leaders that are leading change and innovation a chance to share. The podcast covered topics including how 糖心传媒 has evolved during COVID-19, how the current regulatory environment is evolving and the implications of this and where this leaves FIs. Matt鈥檚 podcast will be released in due course and we will be sharing it across our social media platforms, so make sure to keep an eye out for that!  

AI and Ethics Panel chats with the experts 

In other news, Dr was able to share last week on a panel moderated by Ramesh Dontha, Data Transformers on the topic of AI and Ethics. Increasingly, as you know, companies are leveraging data and artificial intelligence to create scalable solutions 鈥� but they鈥檙e also scaling their reputational, regulatory, and legal risks. Fiona, alongside AI exports Cortnie Abercrombie and Fion Lee-Madan unpacked the conversation further. To discuss more around this subject, reach out to Fiona on

AI for Services Roadshow with KTN 

Sticking with Fiona, she recently pitched at KTN:听AI for Services Roadshow.听The aim of the event听was听to discover the innovation taking place across the UK in the professional and financial, insurance,听accountancy听and law sectors.听Kainos,听Adoreboard听and Analytics Engines are in amongst the few other companies听that听also represented听Northern Ireland in the AI for Services Tour.听To listen to听Fiona鈥檚 pitch,听click here:听.听听

Growing team at 糖心传媒 

At听糖心传媒听we are delighted to announce that we have grown the team by 3 new members this听week. We听are taking part in听Invest Northern Ireland鈥檚 Graduate to Export scheme听alongside a number of ambitious, scaling companies听putting听people into international markets. 听Two new International Business Development Executives have joined the team, Brendan听McCarthy听and Michael Lynch,听who will both respectively spend 6 months working in our Belfast Headquarters before Michael heads off to Japan and Brendan to New York for 12 months. In market,听they will听help us听engage听clients we already have in听the region听and听scale the business听overseas. We are also welcoming our听brand-new听Graduate Research听Associate and听Writer to the team, Roisin Floyd, who will听take on the challenge of听going deeper on many of the听data and industry topics our clients are passionate about.听听Feel free to connect to any of these new team members on LinkedIn as I am sure they would love to talk to you about their听new,听key听roles within听our听rapidly-growing听team!听

Upcoming events 

At 糖心传媒 the events calendar never sleeps! Firstly, Stuart Harvey will be sharing on a panel in conjunction with Invest NI and American Banker. Alongside Dave Linsday, Vox Managing Director and Data Practice Lead and Simon Cole, CEO of hivera, they will discuss how they can help banks accelerate digital transformation to overcome the challenges of legacy systems. Together they will provide CIOs, , , and  valuable insight and best practices for using  and  to break new ground in . Register for the event .

Stuart also is contributing to a webinar on the topic of ‘饾棝饾椉饾槃 饾榿饾椉 饾棽饾榾饾榿饾棶饾棷饾椆饾椂饾榾饾椀 饾棻饾棶饾榿饾棶 饾椌饾槀饾棶饾椆饾椂饾榿饾槅 饾棶饾椈饾棻 饾棻饾棶饾榿饾棶 饾棿饾椉饾槂饾棽饾椏饾椈饾棶饾椈饾棸饾棽 饾棾饾椉饾椏 饾棶饾椈饾棶饾椆饾槅饾榿饾椂饾棸饾榾’, brought to you by . 
 
Register for the webinar to find out: 
 
– How to establish data quality 
– How to implement effective data governance 
– The benefits of combining data quality and governance 
– The best technologies and tools for these tasks 
– How to ensure accurate and meaningful analytics 
 
To register for this event on the 22nd of April, click

For more on AI and Ethics webinar, Data Quality or just an introduction to our new team members, you can find us on ,  or  for the latest news. 

The post 糖心传媒 Round-Up: From speaking on the Finnovate Show to welcoming our new Graduate to Export team members! appeared first on 糖心传媒.

Join us at the  which will explore how financial institutions are shifting from defensive to offensive data management strategies, to improve operational efficiency and revenue-enhancing opportunities.

An event that will be putting the business lens on data and deep-diving into the data management capabilities needed to deliver on business outcomes.

Agenda will be released soon and we will update this page. In the meantime, why not click here for our updates or find us on ,  or  for the latest news.

The post Data Management Summit UK | 02/12 appeared first on 糖心传媒.

Data quality has been a perennial problem for financial institutions for many years, but this needs to change as firms become increasingly reliant on accurate analytics to deliver business opportunity and competitive advantage.

New approaches to data quality can help firms up their game and significantly improve their analytics capability.

Adding the processes, controls and responsibilities of data governance takes them a step further by ensuring the quality and security of data used across the organisation.

If your organisation is falling short of achieving timely and meaningful analytics, or is doing well but could do better, join to find out how to establish the underlying, yet all-important, essentials of data quality and data governance.

Register for this DMI Data Management Insight webinar to find out:

• How to establish data quality
• How to implement effective data governance
• The benefits of combining data quality and governance
• The best technologies and tools for these tasks
• How to ensure accurate and meaningful analytics

You can find more recent content on this topic here.

The post DMI: How to establish data quality and data governance for analytics | 22/04 appeared first on 糖心传媒.

Join us at the which will explore how financial institutions are shifting from defensive to offensive data management strategies, to improve operational efficiency and revenue-enhancing opportunities.

An event that will be putting the business lens on data and deep diving into the data management capabilities needed to deliver on business outcomes.

Agenda will be released soon and we will update this page. In the meantime, why not click here for our updates or find us on ,  or  for the latest news.

The post Data Management Summit USA | September 2021 appeared first on 糖心传媒.

Join us at which will bring together the global data management community to share lessons learned, best practice guidance and latest innovations to emerge from the recent crisis.

Join us online to hear from leading data practitioners and innovators from the UK, US and Europe who will share insights into how they are pushing the boundaries with data to deliver value with flexible but resilient data-driven strategies.

Click to access the agenda.

Click here for more by 糖心传媒, or find us on ,  or  for the latest news.

The post Data Management Summit | 28/04 appeared first on 糖心传媒.

As soon as a firm grows to a size where it needs enterprise IT architecture, changes 鈥� understandably – need to have greater scrutiny, and that takes time.

Time, however, is a double-edged sword. If a change takes too long to implement, the opportunity is missed, and more agile competitors can out-flank you. Secondly, failing to make that change can lead to a resigned acceptance in the company鈥檚 culture that actively stifles innovation.

Many of these problems arise when firms think about change where all the budgets are located. It stands to reason to think that those big changes mean big budgets, but try turning this on its head: look at where the changes are most needed. This is the approach that refers to as 鈥渕arginal gains鈥� – looking at any change that can give any margin of improvement and getting it implemented.

Starting small doesn鈥檛 mean lacking ambition.

Thinking big 鈥� winning Olympic gold 鈥� was the motivator for pursuing change that matters. Yet British Cycling didn鈥檛 turn that big ambition into a series of big infrastructure projects, like fleets of support vehicles or several new velodromes.

“By analyzing the mechanics鈥� area in the team truck, we discovered that dust was accumulating on the floor, undermining bike maintenance. So we painted the floor white, in order to spot any impurities. We hired a surgeon to teach our athletes about proper hand-washing so as to avoid illnesses during the competition (we also decided not to shake any hands during the Olympics). We were precise about food preparation. We brought our own mattresses and pillows so our athletes could sleep in the same posture every night.“

None of these changes represents a massive investment in infrastructure, but they鈥檝e led from one medal in 76 years to multiple Olympic golds. The same approach is being taken right now by leading financial services firms. In a notable case, one London-based financial services firm did exactly this in their COVID response, reducing the usual time to configure rules and query the data from three weeks to three hours.

Like them, successful data leaders are focusing on the pain points of data quality right where they鈥檙e most keenly felt.

• In the teams who are trying to submit reports to regulators;
• By those who are responding to immediate Operational Risk issues, like the impact of COVID;
• By analytics people who are struggling to gain an edge on poor data.

In each case, they鈥檙e identifying a specific problem in one area, for example, client data used in customer communications, choosing off-the-shelf tooling that can be implemented rapidly and then simply getting on with it.

Brailsford鈥檚 team invested in paint and the time to actually paint the workshops. Data management heads are investing small sums in business user-friendly tools on one subset of data so that they can quickly achieve successes without having to overhaul the entire infrastructure of the organisation.

This kind of work means that practically speaking it is no longer a pipe-dream to reduce data quality processes that can take up many weeks down to a matter of hours. It stands to reason that by iteratively implementing these processes, in short, clearly defined sprints, the same processes that drove British Cycling to multiple golds, can yield similar rapid benefits the bigger picture of data quality at financial services firms worldwide.

What about you? Have you adopted a “marginal gains” approach to work that’s paying dividends?

To learn more about how how Data Quality fits into modern Data Ops strategies, catch our as part of 2020’s Data Management Insight event (or read a blog post version here).

The post How To Reduce Your Time Spent On Data Quality Down From Three Weeks To Three Hours appeared first on 糖心传媒.

Many data management professionals have some form of tooling or platform to support their business initiatives but often find it hard to get buy-in for why they still need to invest time and resources in this area.

In by data management blogger Henrik Liliendahl on the passing of Larry English, he refers to English as having 鈥減ioneered the data quality 鈥� or information quality as he preferred to coin it 鈥� discipline.鈥� Liliendahl looks at three main concepts that underpin all data and information quality technologies, and in a moving tribute, inspires us to 鈥渞oll up our sleeves and continue what Larry started.鈥�

So, for anyone involved at any stage in information management, it鈥檚 therefore worth taking the time to consider the impact that English had on the industry and understand what lessons remain today.

Those three concepts are:

1. Quantify the costs and lost opportunities of bad information quality
2. Always look for the root cause of bad information quality
3. Observe the Plan-Do-Check-Act circle when solving the info

Firstly then, on the costs and lost opportunities.

The 鈥淒ata Doc鈥� Tom Redman has a neat test that data managers can conduct on a Friday afternoon 鈥� maybe unsurprisingly called the 鈥淔riday Afternoon Measurement.鈥� Instead of repeating it in detail here, head over to Harvard Business Review and .

In short, assemble like-minded people who know the data and can quickly tell if it鈥檚 right or not, open a beer or two, take four quick steps and a small bit of cost estimation and 鈥� hey presto 鈥� your cost of bad information quality is right in front of your eyes.

What else could you have done with that money? Anyone in financial services knows that it鈥檚 harder to get budget for something than it is to eliminate an operational cost, but as this very simple business case will show, it鈥檚 not actually that hard to demonstrate after a beer on a Friday afternoon with the Data Doc.

Secondly, the root causes.

One of the best ways of discovering the root cause is the 鈥渇ive whys鈥� pioneered by Toyota (there鈥檚 a good guide to it ).

Maybe after your Friday Afternoon Measurement, pick some of your data problems and set your people the task of asking 鈥渨hy鈥� five times, with the clear instruction to get the causes as specific as possible. Summarise, prioritise and then look for ways to eliminate those problems.

Lastly, the 鈥淧lan-Do-Check-Act鈥� (PDCA) loop

(Again, a super guide )

… was pioneered by Dr William Deming as a way of uncovering why some processes or products are underperforming. In data and information management, being able to measure what impact you鈥檝e made is a critical feature both as a reporting avenue to senior stakeholders and as the first stage of the next PDCA loop.

Simply offering up dashboards and visualisations to show what鈥檚 happened or happening is just one part of it. The slickest PowerBI dashboard might well showcase where the data quality is, but unless it鈥檚 also demonstrating where the next priorities and best-recommended actions are, it鈥檚 not actually a loop of continuous improvement.

In summary, it鈥檚 worth looking back at what Liliendahl said about Larry English: he 鈥�…pioneered the data quality 鈥� or information quality as he preferred to coin it 鈥� discipline.鈥� Fundamentally it鈥檚 about being disciplined, but in the right areas!

How could reimagining data quality make a difference to your organisation?

Where can 糖心传媒 help you on your data management journey? We know your time is precious so if you’d like to find out how we can help deliver self-service data quality to your business, simply book 15 minutes of Kieran’s time by choosing a slot .

To learn more about how how Self-Service Data Quality is the best approach to developing a next-gen data management strategy, catch our from 2020 with key input from CTO, Alex Brown (or read a blog post version here).

Click here for more by 糖心传媒, or find us on ,  or  for the latest news. 

The post Data Quality, Didn’t We Do That Already? appeared first on 糖心传媒.

Last September, Kieran Seaward, our Head of Sales, delivered a keynote at the virtual DMS USA on a data powered economy. In his keynote he unpacked:

• The impact of COVID on shifting business priorities, focussing on how existing data management processes and data architectures have changed 鈥� as well as problems encountered along the way听
• Case studies demonstrating best practice approaches to managing data-driven processes and how to create impact and add value in a post COVID era

We are still living with the many challenges presented by COVID-19, including a wide range of changes to the way business can be conducted. At 糖心传媒 we have been really encouraged that engagement with the market is still strong; since March, and the start of many lockdowns, we鈥檝e conducted many hundreds of calls and meetings with clients and prospects to discuss their data management and business plans. This article is based on a lot of our key findings from these calls and reflects the priorities many data-driven firms

Data will power the next phase of the economy 鈥� good or bad?

As global economies look to get back on their feet, it鈥檚 clear that data quality is more important than ever before. Whether it鈥檚 data for citizen health, financial markets, customer or entity data, or any other type, economies and firms will either be powered by a solid platform of high quality, reliable data, or they are going to grow more slowly, built on poor data quality. It鈥檚 not an overstatement to say that what the next phase of economic growth looks like will rely entirely on the decisions and actions we take now.

Kieran鈥檚 keynote was underpinned by the fact there鈥檚 never been a greater need to get your data quality in order. With some firms really grasping this opportunity, change is visible. However, many have encountered the same old problems with the state of their data, and this can inhibit change.

It is necessary to get data quality under control?

Kieran highlighted that pre-pandemic, commented that poor data costs on average between 15-25% of revenue. This figure reaffirms that there is no better time than now to improve the data quality foundation. The long-term future looks bleak if it is built on the quality of data that we have right now!

What is the importance of a foundation of good data quality?

Both before and since the financial crisis of 2008, there have been many conversations reiterating the importance of data quality foundations. Moving forward particularly after COVID-19, a data quality foundation can not only help you get off to a positive start amidst uncertainty, it can also ensure resilience for the future.

Kieran referred to the many conversations that he has had with wealth management, asset management and insurance firms this year. Following hot on the heels of the most innovative investment banks, more and more of these firms are seriously considering what a data governance and data quality framework should deliver.

Redesigning those data architectures that aren鈥檛 delivering

Kieran went on to detail that there have been a large number of firms who have told him that the 鈥榃aterfall鈥� process of logging tickets into software development and IT to improve data quality simply isn鈥檛 moving quickly enough for them, or with the right level of accuracy, and as a result, they鈥檙e evaluating DataOps processes and tooling. Stuart Harvey, CEO at 糖心传媒 spoke in-depth on this at the European Summit, about having every part of the data management journey at the table on data issues, and this is something one client we signed during the UK lockdown is now putting in place as their data management foundation.

On the growth of DataOps-style approaches, Kieran said:

We鈥檝e been encouraged by the number of firms we鈥檝e spoken to who are keen to start moving away from script-based rules. They鈥檙e federating data quality management out from centralised IT and into the business teams so that business users 鈥� who already know the data and what good looks like 鈥� can self-serve for high-quality, ready-to-use data in their line of work. This covers everything from building rules themselves, through interrogating the data itself in dashboards, right down to making or approving fixes in the data within their domain.

What comes first? Data Governance or Data Quality?

Kieran recently wrote a blog on this topic as it he noted that it comes up in every client engagement! To illustrate the importance of data quality, he gave an analogy:

Imagine you are a kid, and your mother has asked you to tidy your bedroom. Your mum returns a few hours later and the room is still a mess – would you get away with saying 鈥�Yes Mum, but look 鈥� I鈥檝e made an inventory of where everything should be.鈥� I imagine the response you鈥檇 get would be something along the lines of 鈥�well done but can you please tidy your room now?!鈥�

Kieran used this story to draw attention to the fact that it is vital to consider data quality first and foremost, as having a large inventory with disorganised data will lead to key data being either inaccessible or difficult to find.

Making a holistic approach to data management

There are a number of building blocks that make up a holistic approach to data management including data quality, master data management, business glossary/data dictionary, metadata management, and so on. As Kieran reiterated in his keynote, using intelligent integration via APIs, it is now possible to build the next generation of data management platform orchestrate by leveraging 鈥榖est of breed鈥� technology components from a multitude of capable vendors. For example,  recently we have explored a more orchestrated approach with vendors like Solidatus, where 糖心传媒 provides data quality and Master Data Management, and Solidatus provides the Governance and Lineage pieces.

Start small, think big

Kieran鈥檚 session reinforced that if you are exploring introducing new capability/ functionality, completing a proof of concept is a well-proven, low-risk means of proving the efficacy of the software and associated professional services. If the scope is well considered and defined on a specific use case that is causing pain, the quick turnaround of results has the potential to create real impact.  This real impact will ultimately help to make the business case a lot stronger.

This is how 糖心传媒 has engaged with the vast majority of our clients and we have successfully delivered remote proof value projects during lockdown that are now being rolled into production.

To use the analogy about tidying your room… Once you start to clean your desk or wardrobe, you can quickly see that cleaning the rest of the room doesn鈥檛 seem as daunting a task, if you break it into chunks.

In conclusion, at 糖心传媒, we believe that it doesn鈥檛 matter where you start, so long as you make the start!

Pre pandemic, MIT Sloan Management Review commented that poor data costs on average between 15-25% of revenue. This figure reaffirms that there is no better time than now to improve the data quality foundation.

Our platform can help you, get in touch today.

In the next blog, we will be unpacking more themes from Kieran鈥檚 keynote 鈥楢 Data-Driven restart鈥�; we will be looking at taking a long-term view; seeking impact that matters, and finally on the budget and implementation approach.

If you want to watch Kieran鈥檚 keynote in full, you can do by checking out this

The post Data will power the next phase of the economy: DMS USA lookback – Part 1 appeared first on 糖心传媒.

At the recent  we held a giveaway competition for the chance to win an independent bookstore voucher – 拢100 to Daunt Books in London, or $100 to Strand Book store in NYC.

We asked data management professionals attending the event to give their biggest data quality or matching challenge. Below you can find a summary of all the most popular answers together with a few quotes from the 糖心传媒 team along the way鈥� 

Problem #1 鈥� Cooperation: Business & Data Need To Get Along 

The main recurring theme was that cooperation between business and data teams is vital for requirements to be ready and rapid to deploy. 

, Marketing Manager, commented, 鈥淐ommunication is key in all lines of business, and in data it鈥檚 no different. We鈥檝e found that smoothing the channel of communication between business users who know what the data should look like and be used for, and technical teams who are often responsible for securing and protecting it, is often best achieved by giving those business users access to a lo-code platform that doesn鈥檛 require coding knowledge.鈥� 

Problem #2 鈥� Resources: I Need More Power,  

Resources was a major issue that kept coming up. Whether it鈥檚 in defining rules, or fixing breaks, another common theme was the sheer amount of human resource needed to fix the problems systems are surfacing.  

CTO , said, 鈥淭he demand for bulk-fixing data quality problems is something we鈥檙e seeing more and more. Machine Learning is definitely making this easier from the perspective of recommending the best way to fix data, enabling businesses to know where and when to target the resources they have available at their disposal.鈥�

Problem #3 鈥� Standardization: The Problem With Standards These Days 

Standardization irked quite a few people, whether because of the lack of it, the process of it, or having to reconfigure internal systems to meet external vendors鈥� demands. Indeed, this is a common point on most data management agenda as firms and professionals try and address the question of standards from all perspectives.

鈥淩apidly configuring data to different standards is one of those data problems that people have become accustomed to existing. Something that鈥檚 hard or impossible to change鈥� chipped in CEO 鈥淭his really comes across as firms try to speed up their data management processes. This is one reason why we鈥檝e focused on the end-user of the software throughout our software development journey.鈥�  

Problem #4 鈥� Responsibility: Who Owns This Mess, Anyway? 

Lastly, we saw the issue of ownership and accountability raise its head. 

鈥淓ven in today鈥檚 tech-enabled age, if people don鈥檛 have a structure where stewardship and ownership are addressed, it鈥檒l slow down any data management strategy鈥� added Client Services Head, . 鈥淭he answers to the competition demonstrate that winning the case for data quality is more than just a choice of the right software vendor. A 鈥榩roof of concept鈥� can therefore be seen to be as much about the choice of software as showcasing a maturing data culture, where business users are equipped with a platform for fixing data instead of it always being a problem they can鈥檛 solve.鈥� 

Thanks to all those who entered, and to for making it easy for attendees to find the contests and submit their entries. 

ICYMI: Our Head of Sales, Kieran Seaward delivered a keynote entitled ‘A Data-driven restart’ at the Data Management Summit USA Virtual. If you missed the keynote, check it out .

The post Biggest Data Quality or Matching Challenge? Here are the answers! appeared first on 糖心传媒.

• A panel discussing exploring the challenges associated with getting data into the hands of those that need it and will explore approaches to data governance and data quality issues, to make the process of using and sharing data, as frictionless as possible.

• A Keynote session looking at ‘a data-driven re-start’ that will cover the impact of COVID-19 on shifting business priorities and focussing on how existing data management processes and data architectures have changed 鈥� as well as problems encountered along the way 

Check out the full agenda at and register your place by clicking on the image below:

Taking place on 29th and 30th September with daily live keynotes, live speaker chat, Q&As sessions and pre-recorded content available; the Data Management Summit USA Virtual will explore how sell side and buy side financial institutions are navigating the global crisis and adapting their data strategies to manage in today鈥檚 new normal environment.

Click here for more by 糖心传媒, or find us on ,  or  for the latest news.

The post DMS Virtual USA | 29/09/20 – 30/09/20 appeared first on 糖心传媒.

In his previous blog on Good Data Culture, our Head of Client Services, Luca Rovesti, discussed taking first steps on the road to data maturity and how to build a data culture. This time he鈥檚 taking a look at some of the biggest challenges of Data Management that arise once those first steps have been made 鈥� and how to overcome them. Want to see more on this topic? Head here.

One benefit of being part of a fast-growing company is the sheer volume and type of projects that we get to be involved in, and the wide range of experiences 鈥� successful and less so 鈥� that we can witness in a short amount of time.

Without a doubt, the most important challenge that rears its head on the data management journey is around complexity. There are so many systems, business processes and requirements of enterprise data that it can be hard to make sense of it all.

Those who get out of the woods fastest are the ones who recognise that there is no magical way of solving things that must be done.

A good example would be the creation of data quality rule dictionaries to play a part in your data governance journey.

Firstly, there is no way that you will know what you need to do as part of your data driven culture efforts unless you go through what you have got.

Although technology can give us a helpful hand in the heavy lifting of raw data, from discovery to categorisation of data sets (data catalogues), the definition of domain-specific rules always requires a degree of human expertise and understanding of the exception management framework.

Subsequently, getting data owners and technical people to contribute to a shared plan that takes the uses of the data and how the technology will fit in is a crucial step in detailing the tasks, problems and activities that will deliver the programme.

Clients we have been talking to are experts in their subject areas. However, they don’t know what 鈥渂est of breed鈥� software and data management systems can deliver. Sometimes, clients find it hard to express what they want to achieve beyond a light-touch digitalisation of a human or semi-automated machine learning process.

The most important thing that we鈥檝e learned along the way is that the best chance of success in delivering a data management programme involves using a technology framework that is both proven in its resilience and flexible in how it can fit into a complex deployment.

From the early days of ‘RegMetrics’ 鈥� a version of our data quality software that was configured for regulatory rules and pushing breaks into a regulatory reporting platform 鈥� we could see how a repeatable, modularised framework provided huge advantages in speed of deployment and positive outcomes in terms of making business decisions.

Using our clients鈥� experiences and demands of technology, we鈥檝e developed a deployment framework that enables rapid delivery of data quality measurement and remediation processes, providing results to senior management that can answer the most significant question in data quality management: what is the return on investing in my big data?

This framework has enabled us to be perfectly equipped to provide expertise on the technology that marries our clients鈥� business knowledge:

• Business user-focused low-code tooling connecting data subject matter experts with powerful tooling to build rules and deploy projects
• Customisable automation that integrates with any type of data source, internal or external
• Remediation clinic so that those who know the data can fix the data efficiently
• 鈥淐hief Data Officer鈥� dashboards provided by integration into off-the-shelf visualisation tools such as Qlik, Tableau, and PowerBI.

Being so close to our clients also means that they have a great deal of exposure and involvement in our development journey.

We have them 鈥榓t the table鈥� when it comes to feature enhancements, partnering with them rather than sell and move on, and involving them in our regular Guest Summit events to foster a sense of the wider 糖心传媒 community.

It鈥檚 a good point to leave this blog, actually, as next time I鈥檒l go into some of those developments and integrations of our 鈥渟elf-service data quality鈥� platform with our data discovery and matching capabilities.

Click here for the latest news from 糖心传媒, or find us on ,  or 

The post Tackling Practical Challenges of a Data Management Programme appeared first on 糖心传媒.