5 min readFeb 1, 2022

Chief Data Officers recognise the importance of data in making sound business choices, but often struggle to link data to specific business advantages and outcomes.

In a recent article from Gartner, it emerged that Chief Data Officers recognise the importance of data in making sound business choices, but often struggle to link data to specific business advantages and outcomes.

Moreover, most data and analytics leaders have difficulty defining, articulating, and communicating the commercial value of data and analytics, and business executives are frequently suspicious.

You must sell your vision and plan to the business community to influence stakeholders, and clearly show the business and personal impact for those involved. It’s easier said than done, so here is a 6-step approach to kickstart your strategy.

With this article, based on Gartner’s blog, we aim to expand on their points with our view and opinion

Step 1

Present a tenacious vision that links the stakeholder’s ambitions intellectually and emotionally. Instead of focusing on delivery progress measurements, concentrate on impact metrics: measurable business consequences and value, completion of tasks and production of technical output . Stop concentrating just on the data and instead use half of your time to establish the value story or thinking. Follow the SMART strategy, which states that business benefits must be specific, measurable, attainable, relevant, and timely.

Foundations can take quite some time to be put in place so when developing projects at scale, organisations need to remember that changes won’t happen overnight. The inability to keep stakeholders engaged is a common reason why projects fail.

The keyword in this scenario is transparency and making projects transparent to senior executives within the business. Defining a start starting point and an endpoint, and measurables, along that journey, is key to keeping stakeholders engaged. Once organizations can then measure the project performance, they can monitor it; when they can monitor it, they can report on it.

Step 2

Identify barriers, challenges, issues, or hazards clearly and concisely (e.g., incumbent technical staff want to protect their positions, limited availability of skilled staff may inhibit the ability to execute, or there are power struggles over who owns the budget for data and analytics initiatives).

ML applications can present several barriers to adoption such as not having a standard approach to connecting the digital world to the physical world and hence not having common codes for translating data. Machine learning algorithms usually require large volumes of data to learn from to achieve required accuracies and especially with ideation, the key challenge is to make sure you are focusing on value and building that business case, to make sure the business understands it, users engage with it as well and know how to scale it.

Present the consequences of these difficulties and make recommendations for how to address them. Develop a data literacy programme to enhance participation and generate analytics prototypes using actual business data to inspire business thinking and popularise new ideas, for example, if the company’s data literacy is poor.

Read more on How to Ramp up Your AI Projects here

Step 3

You must tie business value outcomes to the underlying data needed to enable Data & Analytics solutions while designing them. Connect the data that the company requires to answer critical business issues.

The exact inquiry your company should ask is determined by your most well-informed priorities. Clarity is necessary. “How can we cut costs?” is an example of an excellent question. or “What can we do to boost revenue?” Questions that go deeper, such as “How can we boost the productivity of each member of our team?” are even better. “What can we do to increase the quality of patient outcomes?” “How can we drastically reduce the time it takes to bring a product to market?” Consider how critical functions and domains may be aligned with your most important use cases. Iterate through real-world business scenarios to determine where the value exists. In the real world, where budgets and time are limited, analytic exercises for vaguer queries like “what patterns do the data points show?” seldom pay off.

Customer contact data from the customer relationship management (CRM) system, for example, can be merged with financial transaction data from the finance system. This is done to create a profile of consumer behaviour that marketing may utilise to create a new marketing campaign that targets the most profitable client category.

Step 4

Create a Data & Analytics roadmap that shows the important business-facing data and analytics solution deliverables and their milestones. Show the deliverables that must be completed over time to reach the desired outcome.

Your data and analytics roadmap helps to foster a common vision of the strategy among sponsors, team members, and consumers by keeping everyone on the same page regarding the practical actions. By describing exactly what is being done, when changes will occur, and the value that will be given to the business as a consequence, a roadmap eliminates ambiguity and aids in the management of change.

Step 5

The initial investment and continuing maintenance expenses must be justified in terms of the predicted ROI when developing the business case for D&A. The business case for the programme should be forward-thinking and future-oriented, with a net positive financial return.

Step 6

Assign direct accountability, set timelines, and explain why the following actions are necessary. Establish an initial cooperation workshop, for example, and assign the project leader the duty of bringing together business and technical project participants to begin studying the available data.

Putting together a fantastic team is similar to making a gourmet dish: you need a combination of high-quality components and a splash of enthusiasm. Data scientists, who assist in the development and application of complex analytical methods; engineers with expertise in microservices, data integration, and distributed computing; cloud and data architects, who provide technical and systemwide insights; and user-interface developers and creative designers, who ensure that products are visually appealing and intuitively useful. You’ll also need “translators,” or people who can bridge the gap between IT and data analytics and business choices and management.