Understand the paradigm shifts of the digital economy and see how to drive your transformation towards an intelligent enterprise powered by a data-enabled learning organisation in a simple step-by-step guide for a data management strategy
Why should you care?
Enterprise data is often referred to as the new currency. Why is that so?
In a global economy an individual customer is often not really known (beyond a name, an address, a payment method, and a purchase history) to the business. This in turn makes it difficult for a company to develop new products or services that meet or exceed market demands.
Now, for a moment, think about that old fashioned, small butchery around-the-corner type of shop. The owner of that butchery knew his/her clients personally, had insight into their preferences, their habits and moods, their expectations, their credit worthiness, and often even understood their entire personal and family background. The better that business knew their customers the better their chances of building long-lasting customer relationships and, consequently, sustainable success.
The latter insight into the relationship between knowing your customers and the success of your business is of course nothing new. Gathering customer information to make better products and services is an old strategy. Only problem is that traditional approaches aimed at gaining insight are costly, time-consuming, and often only based on a relatively small number of customers. This is particularly true for many traditional manufacturers using indirect sales channels or distributors.
Today new technologies like the cloud, IoT, machine learning, and AI enable companies of all sizes to quickly process vast amounts of information and to make sense of it. With that the paradigm shifts from data insight of a selected-few towards data-enabled learning organisations.
With the raise of the internet and social media customers have become much more informed and aware of alternative choices. Despite that there is that one thing that has not changed for an individual customer. It is the appreciation of that warm feeling coming from being known, understood, and be treated as someone special and unique. Why else do we all aspire ranking high in modern loyalty programmes?
The paradigm of the Intelligent Enterprise explains how the best of both worlds can be brought together: reaping the benefits of digitisation (data-enabled learning, speed, reach, flexibility, scalability) plus being able to create a compelling customer experience. For long the successful entrepreneurs understood what their customers aspire to (sometimes even before customers expressed the same) and knew how to best deliver it. This equation is unlikely to change anytime soon. However, in a digital economy, companies can no longer solely rely on a genius entrepreneur but require an all-encompassing, data-enabled learning culture and organisation.
It is exactly for that reason that data in a digital economy is considered to be a, if not the, main asset of any enterprise; small or large alike. Chances are data may soon appear as an asset in corporate balance sheets.
Increasing digitisation of processes, human interactions, and machines are generating data in structured and unstructured formats at an unprecedented speed and volume. This happens inside and outside of individual enterprises. So much so that companies are running out of capacity to store all of it. At the same time many companies seem to understand less and less about their customers and businesses. Astoundingly, this appears to be mostly prevalent in those businesses that are commonly considered being very close to its customers and the market – Medium Enterprises.
Surprised? One would expect the opposite given the advancement of modern technologies and the ubiquitous access to information.
How can companies deal with an ever-increasing speed of data creation taking place at dispersed locations and across multiple applications, systems, platforms, and clouds? Obviously, the traditional capturing, storing, and pre-defined processing of data needs to be reviewed in the light of today’s reality and business leadership’s needs.
An enterprise-wide data management strategy ensuring seamless data access, integration, and orchestration focused on expanding digital capabilities will guide the way from handling (big) data to operationalise data for intelligent analysis. This is how data can be leveraged as the core of an intelligent enterprise.
What are the challenges?
So far, I hear many saying that all of that makes sense: “but our current situation is full of roadblocks …”. And those of you feeling like that are not alone. Let’s look at what businesses see as underlying challenges complicating the intelligent use of data. The same source from IDC sheds some light into this.
Clearly, these issues need to be addressed if companies are embarking on a journey to make more effective and intelligent use of data.
What to do?
Before looking at the how-to, make sure to understand what exactly it is you are aiming at given your specific context. Generally speaking, seamless data access, data integration, data quality, and data orchestration are seen as prerequisites to expand on digital capabilities.
In a digital economy, businesses must move away from just collecting, storing, and processing data in pre-defined ways. Focus needs to be on making effective use, i.e. operationalising data giving way for intelligent, flexible, and spontaneous data analysis driving direct impact on business performance. Non-intelligent, routine data management procedures should be automated as much as possible.
Undoubtably, data is a strategic asset. Used wisely and leveraging new technologies it allows for data-enabled learning across an entire organisation yielding results well beyond what insight from a selected-few can achieve:
- monitor and optimise performance beyond financial metrics
- drive customer experience
- find growth opportunities
- identify cost savings
Puzzled now? Asking yourself how to get from here to there? Good news is there is a proven track to follow. Developing an enterprise-wide data management strategy will guide the way.
Elements of a data management strategy
At this point it is important to distinguish between data management and a data management strategy. Whilst both are interlinked, they are not the same. It is the strategy that should drive the operational execution.
Best practices for data management are well established and documented in what is known as the DMBOK2. For those interested in more details please follow this link https://technicspub.com/dmbok/.
Best practises to develop a data management strategy, however, are not (yet) commonly established. Below is what I have come to see working well if one is to prepare for the development of a data management strategy.
Before going further keep one thing in mind. Developing a data management strategy must not be a costly, year-long exercise. Think in weeks, be and remain agile. Go for quick wins. Don’t strive for 100% perfection at the first go. Accept the fact that a good data management strategy will change as often as the business does. Life in a digital economy is changing fast and disruption is becoming more of a rule than an exception.
Step 1 – Align overall business strategy with data management strategy
Naturally, this is the most important step as it drives everything further down but more so because it lays the foundation of how well intelligent use of data across an organisation drives business performance. Done well, both business and data management strategies influence each other.
Think about business asking data: “We have bought this new line of business and want to offer their products to our existing customers. How can we get this done?”
Or, think about data asking business: “We can now predict the likelihood of customers to be interested in our new offering XYZ. Can we monetise this capability?”
Step 2 – Drive organisational change around data
Considering your current maturity of processes, people, and structures versus business ambitions what is it that needs to change?
Ask: “How can an enterprise-wide culture of data-enabled learning be introduced?”
The answer outlines if and what needs to change around data governance, i.e. who is responsible for what, who has access to what, how can confidentiality and compliance with legal requirements be ensured, and so on.
Step 3 – Leverage and manage data for strategic purpose
This is to confirm what information is needed to support both the business strategy and the aim of enabling intelligent data analysis. It is to understand whether it is about master data, transactional data, structured or unstructured data, analytical or modelled data, and most importantly, data quality requirements.
Think of scenarios like
- “Our customer R has a positive credit record with our company but when we started to consider external sources we realised she defaulted on two loans.”
- “Supplier S is demanding higher prices to continue doing business with us. We have to open up a tender process but need reliable information about product quality as part of our decision making process.”
Step 4 – Integrate different sources and coordinate data for meaningful use
Once data requirements are identified, the next questions are where can that data be found, where should it be stored, and how can meaningful consistency be ensured.
Think of scenarios like
- “We know that customer A has different customer numbers in our various divisional/regional systems. How can we achieve an enterprise wide view of this single entity?”
- “Different systems in our organisation have different validation means for address data. How can we ensure a consistent reliable quality of contact data?”
Step 5 – Define technology requirements
This is about the variety of different data formats available and how to make sense of it. Data may be provided from relational or columnar databases, all sorts of documents, XML, social media, pictures, and voice. Which technology is needed to make sense of this?
Think about business asking data: “We keep recordings of our customer service centre calls. Can we make sense of it beyond compliance or legal requirements?”
Step 6 – Identify quicks wins for a first implementation plan
With the data management strategy being developed quicks wins should be identified and respective implementation plans should be provided for decision making. This for rapid execution and benefit realisation.
Conclusion
In a digital economy data is the new currency and builds the core of the intelligent enterprise of the future. The ability to make intelligent use of all relevant data, i.e. creating a data-enabled learning organisation must become a mantra for all employees; not just for board members.
Free up capacity of your intelligent workforce by automating routine data management procedures as much as possible. Develop a data management strategy in line with your business strategy to guide your way towards becoming an intelligent enterprise. Aim at quick wins rather than perfection in the first go.
Don’t allow the development of a data management strategy to become a costly and long-lasting exercise. Remain agile. The notion of “nothing endures but change” is old but has never been more relevant than today.
Embarking on a data management strategy will lead the way towards a data-enabled learning enterprise and the intelligent enterprise of the future.
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