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How fluid is your data strategy?

(Image credit: Image source: Shutterstock/alexskopje)

In today’s world, it should no longer be acceptable to have merely adaptive data. To win customers and market share, an organization must do far more and predict which strategy will unlock the potential its data has to offer. A company must envision how it will compete against today’s known players and future disruptors. Additionally, it needs to anticipate how government rules and regulations will affect its playing field, and it must protect its brand in hostile environments.

Ask any CIO or CDO and they will tell you that it’s fairly complex.

Advancing Data Strategy via Business Drivers and Tech Enablers

To move an organization onto a more advanced plan of action, CIOs and other executives can think of data strategy in the simple terms of business drivers and technology enablers and how to constantly evolve it. Automation is a business driver that commonly prompts companies to consider new data strategies. As the imperative to run leaner operations grows, enterprises find it valuable to automate business processes to help expedite work that ordinarily takes up long periods of time. A fluid data strategy allows a business to mine the information on how a certain manual function was done in order to automate it. A common tech enabler that actualizes this transformation is Artificial Intelligence (AI). Mimicking the way the human mind works, tools enabled by AI can gather the needed data and build a prototype of the tasks that are to be automated.

Figure 1

Figure-1 illustrates some of the drivers that can shape your data strategy. On the vertical axis, it shows innovation versus risks and regulations, and on the horizontal, centralized IT versus business users, because they inhibit opposite priorities in most cases. The business drivers in the upper half help you increase top and bottom lines, whereas the lower half keeps you from paying hefty fines for non-compliance. The right half represents the priority of your business users and lines of businesses, and the left half is what keeps your centralized IT occupied.

Based on this situation, budget, resources, and predicted future needs, the recommendation would be to focus on just a few interconnected drivers for the next six months. As part of the data strategy, the organization would establish the selected drivers as business goals, allocate specific budgets, bring together teams that understand the impacted systems and processes, and define how to measure success and monitor progress.

Figure 2

In another example, suppose an organization wants to reduce costs through lean IT and create new products based on data insights. At the same time, they also want to identify technologies to enable this new data strategy for the next six months. Figure-2 indicates that the organization may want to focus on the creation of dashboards to show how its products and revenues stack up, along with the building of data lakes and automating of data ingestion from upstream sources. One will help identify strengths and gaps in offerings, and the other will create a platform for the future.

Redefining Data Strategy: The Holy Grail of Marketing

Once it has successfully achieved these goals, an organization may want to redefine its data strategy to take up more challenging goals such as the holy grail of marketing: a “cradle-to-grave” lifecycle journey. That will require allocating new budgets, adding experienced marketing analysts and data scientists to the team, and ingesting new datasets into the data lake from Web analytics, marketing automation, and CRM systems, among others.

With time, an organization can learn to (a) strike a balance between competing priorities and (b) keeping all teams in sync to  achieve new goals every few months as part of fluid data strategy and (c)  monitoring progress frequently. It can become a champion at predicting and defining the right drivers and selecting suitable technology enablers from the likes of Figure-1 and Figure-2 to create custom, fluid shapes outlining an organization’s Agile data strategy.

Figure 3

The new trends observed in the data landscape that will guide organizations in refining their data strategy are indicated in Figure-3.

Business Intelligence

Most business intelligence today is backward-looking and obsolete. Data science and AI give you the tools to mine your data and build models that accurately predict the future. Data science uncovers insights that are otherwise extremely difficult, if not impossible, to achieve. AI helps automate decision-making based on learning.

Data Warehousing

The role of data warehousing has been extended to include data lakes, saving cost and offering the flexibility of the cloud. Data lakes can help reduce computing and storage requirements and costs by ingesting raw data from the data warehouse, performing ETL, and returning aggregates to the data warehouse allowing existing downstream applications to work without any change.

Traditional Master Data Management (MDM)

Most traditional MDM initiatives are starting to be seen as never-ending and as providing little, if any, value. Instead, Agile MDM has emerged as far more productive and useful, with use-case specific minimum viable data, automated data quality improvements, and reference data updates with AI in the data pipeline.

Single Version of Truth

Most organizations have considered creating a single version of truth for some of their enterprise datasets. A few resourceful companies have even used semantic modeling to bring different versions closer. A better approach though, involves having a single source of truth but allowing many versions of truth. For instance, how many customers paid for a particular movie stream will likely differ from how many customers watched it in a given month. The first number is of interest to the accounts department and the second one to marketing, so while they both represent different versions of the truth they originate from a single source of data.

Batch and Files

One more trend we all have seen is the use of real-time streams instead of batches and files. Data’s value decreases quickly with time, so it is best to analyze it in-flight before storing it. Also the more we store, the more data debt (what we need to analyze) we collect. Most of the time, it makes sense to reduce or throw away the unimportant raw data and store only compact summarized or aggregate data, which should be made available as a service to other systems harnessing more value from your data.

In summation, all businesses clearly stand to gain from adopting what can be called a fluid data strategy. Such an approach gives enterprises the flexibility to pick only those business drivers and tech enablers that are relevant to their business plan. It also provides companies with the room to come back and review their choices every couple of months to tweak and rethread their strategy according to new trends and goals.

Mahesh Lalwani, Vice President, Head of Data & Cognitive Analytics at Mphasis (opens in new tab)

Image Credit: Alexskopje / Shutterstock

Mahesh has 25 years of experience in building roadmaps, products, and platforms; creating new GTM strategic initiatives for business growth; and leading teams across Fortune 500 and start-up companies. At Mphasis, Mahesh heads the Data and Cognitive practice, and engages with client leadership.