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3 pillars: successfully becoming a data company

It’s a fact: if you don’t make data the foundation of your enterprise, you will be trying to navigate a dynamic, highly competitive business environment wearing a blindfold. Lifting that blindfold – and successfully using data insights to direct and continuously improve your processes – depends on how well you prepare for the data-driven future.

The benefits of data-driven processes are plenty: think optimized operational processes, agile responses to changes in demand, personalized marketing campaigns and services, automated production systems and engaged, supported employees. You can take these advantages even further, for example, by developing new as-a-service business models, embracing low-code/no-code applications and even monetizing the data you gather.

As exciting as this future sounds, a measured, carefully considered approach is fundamental to your successful transformation.

Pillar 1: Build on the right cultural foundations

As we discussed in our blog post "Enter the ‘data company’: when data becomes a business" – a company that places data at the core of its activities – is more than an implementation project or a new app.

“You have to involve your entire business because it’s an organization-wide transformation,” asserts Kristel Demotte, VP of Data Solutions at Cegeka. “During the process of becoming data mature, many companies choose to bring on board a Chief Data Officer – or CDO: a new C-level executive responsible for defining a strategy, setting up data-related tools and systems properly, and ensuring that insights are actually used by the business to make decisions, improve processes and products, etc.”

Managing expectations is an essential part of becoming data mature. “For example”, adds Sales Manager Jan Kesters, “Enterprises often perceive becoming data-driven as only achievable with Artificial Intelligence (AI), which is seen almost as a sort of digital data wizard. There is a misconception that they can funnel data towards an AI model and the AI model will wave its magic wand and give them great insights. As you foster awareness, it’s important to stay realistic about the outcomes and possibilities.”

Pillar 2: Top down or bottom up: generate business value from the outset

But before worrying about where to find your new CDO, it’s important to note that a ‘big-bang’ isn’t feasible for data transformation projects. “Indeed, the goal isn’t to be completely data driven from day one,” says Kristel Demotte. “In fact, a small beginning will give you the experience, buy-in, support, and real-life evidence that you need to go full scale with the project.”

The goal isn’t to be completely data driven from day one. A small beginning gives you the necessary experience, support and evidence to go full scale.Kristel Demotte, Global VP of Data Solutions at Cegeka

“It does sound like a chicken-or-egg problem, doesn’t it?”, Jan laughs. “It’s easy to get confused by all the advice flying around. Develop a huge, comprehensive data strategy? Bring a CDO on board tomorrow? Hire all the data scientists? Those are large investments that are often impossible without first achieving business value through a first data project.

“There are two directions that data-driven momentum can flow. If executive sponsorship is already in place, the C-level is responsible for making sure that data receives the right attention and support, and that employees are enabled to launch data projects. But if there is no C-level buy-in, it’s necessary to prove small-scale business concepts using data. The resulting cost savings and process optimizations are sure to spark attention – no matter which direction the momentum is flowing.”

Pillar 3: Start small while thinking big

Starting small means identifying less-than-optimal processes and bottlenecks that lead to delays, inefficiencies and extra costs.

“These low-hanging fruit can often be found in manufacturing and professional services, in quality and safety,” Kristel continues. “In finance, order to cash and procure to pay are common domains to start with data. After you select the most valuable use cases, the next steps are identifying the data sets that impact the use case, harmonizing the data, using it to improve the process at hand – and then quantifying its impact in monetary terms.”

This brings to mind another important misconception about data projects: that they are expensive. “That’s simply not the case”, Jan says. “A granular approach ensures that the steps to transformation happen in a logical order that doesn’t require huge up-front investments. As each step is achieved, the resulting value and learnings can be funneled into the next step.”

6 elements of a future-proof data strategy

  1. Fostering an organization-wide, cross-domain data culture;
  2. Executive (CDO) sponsorship;
  3. Ensuring data readiness with the right technical skillsets;
  4. Implementing the right technology: a cloud-first data platform;
  5. Defining a business/commercial approach supported by Data that empowers employees;
  6. Identify how you can differentiate from the competition using your data.

Get it right the first time with the right partner

As an experienced data solutions implementer, Cegeka helps companies select the right approach for their businesses. We also build each reference architecture using a step-by-step methodology based on the needs and maturity of each customer.

“We have a variety of data use cases under our belts that serve as inspiration in order to help businesses identify which projects to start with, and which technologies to build on”, Jan goes on to say. “Our team has developed an approach that clarify business feasibility and value. We combine that with a use case identification workshop and then help build an impactful proof of concept by linking business leaders, the IT team and business personnel together.”

Checklist Data Maturity