Modernising your data and AI platform

In the coming blog series, Capgemini and SAP will be looking at topics that affect organisations around the globe when looking at how they modernise their data & analytics landscapes to implement a “data first” culture and drive innovation through AI & Analytics.

First, let’s align on some basic concepts so we’re all on the same page. Today we talk about technology in service of data and what we mean by this, is that it is about keeping the lineage of data from source through to the point of decisioning. Turning raw data into knowledge and ultimately actionable insights.

The challenge today is to get the right data, at the right time, to the right person or AI application. The speed of extracting value from data today can no longer be supported by data quality and privacy applied at the point of report creation. The Next-Gen Data & AI platform needs to ensure secure, trusted, ethically managed, data democratisation, and is architected with these principles at its core at the relevant steps of the data flow.

What we are seeing in the marketplace, from early adopters of Next-Gen Enterprise Data and AI Platforms, is that organisations are enjoying real-world business impact from getting this right and managing the pitfalls. They are influencing sales, boosting operations, engaging customers, and generating insights for better analysis.

Real competitive advantage is delivering data, AI & analytics at scale by infusing it into the culture and decision-making processes of the organisation.

Over the course of this blog series we will be focusing on key issues for a Next-Gen Data & AI platform and breaking it down into the following areas:

• Data Preparation: A data-first approach to the management and utilisation of data for a modern, ethical and trusted approach to the management of data
• Ingestion: Do you leave the data where it is? If the data is distributed across multiple systems, is it is best to manage data in a federative way – balancing local ownership and a central platform?
• Store: Data persistence within a Hybrid landscape. A look at extraction from ERP systems into data lakes and SQL databases vs data warehouse approaches
• Prepare and Train: Value can be derived through more advanced techniques such as machine learning, but why do so many fail? Why are organisations encountering POC burnout?
• Serve: The changing world of visualisation and analytics tools.

Today’s relationship with data

I won’t refer to it as the new oil, but in the changing world of technology and innovation data has become the new constant. Organisations are increasingly looking to adopt approaches, that create ethical and trusted data assets that enable organisational transformation to a data driven culture.

The data organisations are producing continues to explode in volume, velocity, and variety and there are no signs of a slowdown, while the value and veracity continue to become more and more important.

If exploited correctly data can provide such a high value of return derived from a low production cost (we generate so much data in everything we do!) it is no wonder that it is at the forefront of organisations’ minds.

But what has changed? Do organisations need to transform their relationship with data?

The change is coming from multiple directions, from customer expectations, regulation, and from the volume, variety, and velocity of data itself. The ability for organisations to respond to that change becomes a differentiating factor against their competitors.

What are companies doing about their changing relationship with data?

Companies continue to make substantial investments into the development of experimental and potentially game-changing technologies in order to either invent or reinvent themselves to survive disruption, gain differentiation in the market, and maximise value for their stakeholders.

This means introducing new business models, customer experiences, new products, services, and operations for the future. Technology is not new as a disruptive force, but it is accelerating change like never before.

The need for a Next-Gen Enterprise Data & AI Platform

Next-Gen Enterprise Data & AI platforms must be designed to support the business demand and value proposition of the unknown as well as the known. It is about designing a platform, to embrace change, and to evolve with business and technology innovation.

Many organisations are finding that the technology and data silos of their current landscape are not able to support the evolution or even revolution in data exploitation ahead of them. Successful organisations are embracing modernisation to drive business transformation, powered by data, enabled by technology, and enacted by people.

In doing so organisations are embracing new operating models, leverage existing on-premise solutions, and integrate or even fully replace landscapes with cloud offerings. The next generation platform leverages the innovation of a hybrid architecture.

The challenges are to achieve this revolution in capability, innovation, and competitive advantage, whilst lowering the total cost of ownership, which shouldn’t be confused with the lowest cost of storage.

It can be argued that technology is 20% of a solution, but when technology fails to meet the business value proposition, it becomes 100% of the problem. Putting the business value proposition at the centre of the platform architecture and implementing a people, process, technology and data transformation, will create the data-driven culture which will unlock the future.

Conclusion

The marketplace is constantly changing, and organisations are focused on how they can simplify the management and exploitation of data to drive real-world business value. More and more they are faced with challenges about how they integrate their complex hybrid landscapes together to enable this change whilst achieving the business vision and goals.

Clearly any solution involves technology, but technology can only do so much, and organisations must incorporate new processes delivered by a skilled talent pool.

We look forward to delving into more detail in this blog series and we hope you will join us next time where we will be discussing data preparation and management.

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