Winning the Data Battle

Welcome to part two of this joint Capgemini & SAP blog series presented by David Allison & Fiona Critchley from Capgemini and Dr. Mark Darbyshire from SAP.

Part One saw us set a common understanding of the current marketplace and the importance of having a Next-Gen Enterprise Data & AI/Analytics Platform in place. A platform that is fit for purpose in today’s world and flexible enough to meet the demands of the future.

In today’s blog, we are going to be looking at the management of data; and one thing to note is how much the world we face has dramatically changed from when we first started discussing the structure of this blog series.

Over the last few months we have seen the importance that previously ignored data sets can bring to organisations and how it has become even more important to drive value from all the data sets your organisation has.

Organisations Winning the Data Battle

Organisations talk about becoming data-powered or data-driven and whilst it has become a bit of a cliché it has consistently been proven to unlock business value. A prime example is social media organisations. Data powers everything they do and without its exploitation, there would be no social media industry.

For those organisations who are winning their battle with data, they are seeing their efforts come to fruition in several ways. Pre-COVID-19 the focus might be on improving revenues and margins. Currently, there is a focus on ensuring organisations can navigate their way through turbulent times. How can their data improve their impacted supply chains to get their goods to their customers or where can operational process be improved in order to reduce costs and save jobs or in the worse cases the entire company?

The Data Challenge

Achieving this data nirvana is not easy and there is a myriad of challenges to overcome. There is an ever-increasing demand for data and AI capabilities to enable business innovation, whilst there is an increased focus on data governance, regulations and ethics.

Firstly, a mindset change is needed towards data.

Historically, processes were a differentiating asset for organisations; data was viewed as an expensive requirement to enable business process or a byproduct thereof. A data driven transformation is about changing how organisations, employees and customers think about, value and engage with data.

With this mindset data is then expected to:

• Act as an accelerator to innovation and industrialisation, enabling more extensive use of agile methods
• Act as the single version of the data’s truth to support innovation and industrialisation
• Importantly, be governed. Not all data should be governed equally, but it should be governed appropriately.

Becoming data-powered can be about process simplification, optimization, and automation as well as data-driven actionable insight, data to knowledge to action. To do so organisations will need to evaluate and change several factors. Here we will fall back on the much-loved people, process, technology, and data:

• People – Break down silo’s across IT and the business
• Process – Simplify, optimise and automate
• Technology – leverage disruptive technology capabilities
• Data – Become a data-powered organisation through the industrialisation of data

It is commonly accepted that businesses now see data as having a value within the organisation that can be deemed an asset, but frequently we see that it is not treated as such. With any other asset, you simply wouldn’t allow it to be managed inappropriately in such a way that could damage its intrinsic value. You find the asset, guard it, secure it, leverage, and scale it to drive value repeatedly.

Management and governance of data isn’t about stopping agility but about understanding what that data means, where it has come from, where and how it is stored, and how it is being used. With the world becoming evermore data-focused it means customers, internally and externally to an organisation, expect data to be treated securely and ethically. Falling foul of the legislations that monitor these can have serious implications.

By putting data governance at the core of the Data & AI/analytics platform we are saying upfront the management, quality, and governance of our data is the key to unlocking its value. Data governance is the foundation on which everything data-related is built upon.

Enabling a data powered organisation

As part of Capgemini’s Next-Gen Enterprise Data & AI Platform reference architecture, we incorporate our data trust foundation, as seen above, as a starting point to building an efficient, automated, and effective data platform. At a high level to manage data effectively and to create the right data asset, we need to keep three key things in mind:

1. The ingestion of data into the data platform will require governance, auditability, and security compliance. This includes:

• A definition of the incoming data from a Business use perspective; (Business Catalogue)
• Documentation of the metadata, context, lineage, and frequency of the incoming data
• Security level classification (public, internal, sensitive, restricted) of the incoming data
• Documentation of creation, usage, privacy, regulatory, and encryption business rules which apply to the incoming data

2. Business ownership of data and management of the data:

• The data owner (sponsor) of the ingested data
• The data steward(s) charged with managing the health of data items
• Continuous measurement of the data quality as it resides in the data lake

In smaller organisations, data ownership is often a challenge. One approach to address this is to recognize that governance leads to lower management costs for the data. Another is to compliment the data scientist with a data curator; or the data engineer with a data technician.

3. It is recommended that the policies and processes for the consumption of the data from the data platform are established and adhered to, meaning:

• Publish and maintain a data catalog and business catalogue to all stakeholders
• Configure and manage access to data
• Monitor PII, GDPR and other regulatory compliances for the usage of the data

Conclusion

The aim of data-enabled organisations is to develop a data & AI platform strategy which enables secure, trusted, democratised data. If this isn’t considered, then there’s the strong likelihood of false insight, poor decisioning, and regulatory transgressions – bad for you, your customers, your business.

Instead, embracing a sensible and mature data management strategy will not only enable business innovation and self-service but will instill timeliness and confidence in the results. This is an important step on the route to industrialised AI & Analytics and one that should be built into the foundations of any data strategy.

In the next blog in our series, we will be looking at data storage. Warehouses, physical and logical lakes, lake houses, data mesh. It can be a confusing world out there. We will shed some light on how to approach this area.

If you have a question, please leave a comment or feel free to get in touch with us directly.

David Allison – Head of SAP Analytics, Capgemini UK

Fiona Critchley – Portfolio Lead AI & Data Engineering APAC, Capgemini Australia

Dr. Mark Darbyshire – Platform & Technologies CTO, SAP UKI