The power of an effective Data Strategy

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Jonny Longden
Co-Founder & Conversion Director at
Journey Further
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Krishna Munagala
SVP Global Delivery at
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Frederik Bisbjerg
Executive Director, Digitalization at
Daman National Health Insurance Company

Every organization leverages data in one-way or the other. So what differentiates the successful ones from the rest? The key to success lies in utilizing your data strategy to solve business problems.

Technology advancements like modern Data Warehouse engines or the wave of Artificial Intelligence and Machine Learning are all encouraging. In the midst of these rapid changes, the significance of a data strategy is often overlooked. The technology, after all, is amazing. Imagine how far it can go with a culture to match it stride for stride.

An effective data strategy ensures business alignment, and takes both top-down and bottom-up perspectives into consideration, along with laying emphasis on organizational culture. Companies need to consider data as a strategic asset. Data strategy outlines the road map for prioritization of initiatives, governance and communication.

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What is data strategy?

In its simplest form, data strategy is about aligning the usage of data in a way that best serves business needs. There is no dearth of data in the current times, and we probably have more than we can digest. Hence, it is critical to understand and prioritize efforts to leverage data.

What is the need for data strategy?

Companies have data & analytics projects, which are either complete, in progress, or planned. While certain initiatives are planned at a corporate level, some are at a business unit level and others at an individual team level. An effective data strategy helps bring in alignment across all such initiatives. When the data strategy is defined comprehensively, every data and analytics effort across the organization will result tangible business outcomes.

Is data strategy possible for businesses of any size (small-medium-large) and at any stage (startup -established)?

Yes, data is for all! What could differ from one to another is the approach, scale and priority. The first step to embark on this journey is to get all stakeholders together, understand their pain points, define objectives, quantify the business impact and align the strategy accordingly.

What are the key components that need to be considered when building Data Strategy?

Business needs, stakeholders, data management, governance, quality, decision culture, and technology are key elements. It is important to note that data strategy should be a function of bringing all of them together.

How does data strategy work?

It all begins with a plan to build the strategy. However, culture and execution are equally important. A culture that embraces change, experiments and evolves is critical in current times. When the culture is right, it is natural for stakeholders to come together and execute the strategy in a cohesive manner. It is also important to measure and monitor the outcomes, and have the strategy reflect the dynamics of business.

What is data management and governance?

Data management is a set of activities like acquire, store, manage, share and use data to achieve specific business goals. This data could be first-party, second-party and/or third-party. It is important to understand the value of each of the sources available, with the meaning and purpose attached. Data governance is not about controlling and making it difficult for people. The key is to balance it in such a way that users would trust and benefit from data and not decide to live without it.

How should one handle cultural aspects while defining their data strategy?

Data culture is decision culture. No matter how good the solution or technology is, it serves no purpose if users don’t adopt it. So, it is critical to form core groups, identify champions, encourage teams to share success stories, and create a buzz about the business impact that data is enabling. Also, ensure that the solutions are embedded into business processes and not siloed, as siloed implementations shall prove to be an overhead for users.

Why should one consider top-down and bottom-up approaches?

While a top-down approach helps one understand the business objectives, the bottom-up approach ensures alignment. By having the leadership help define the business objectives and then looking at the data landscape, management, governance and inputs from front-line teams would help define data imperatives that align to business objectives.

How can one achieve 'better' outcomes through data strategy?

It all starts with the right stakeholders and the right level of collaboration. We have observed that organizations that make early investments in data strategy and understand the complex relationship between their data, their business processes and their long-term business goals are often successful. This requires time, effort and commitment from senior leadership.

How can one be sure that they have right technology solutions and services to support their business needs?

It is when you ask the below questions, that you will realize the limitations, if any –

• Do you have access to the data you need?

• Is the data reliable and devoid of any quality issues?

• Is the data giving you all the insights needed to make informed decisions?

• Does the platform help you plan for the future?

• Is the platform helping you deliver business process efficiencies?

• Is your platform evolving per the business needs?

If the answer to any of the questions listed above is no, then there is an opportunity for you to assess the current landscape and collaborate with the right team/partner to optimize the investments you are making in data & analytics.

Would having teams co-located offer advantages?

When teams are aligned and are provided with the right tools and accesses required, there should not be any impact even if they work remotely. What is important to have is a process, which will measure and monitor the outcomes. It is equally important to ensure that the teams remain engaged enough to be focused. Location is digitized in the post-COVID world and this trend is likely to continue and hence having a team with the right skills and culture is key.

What are the key roles for developing and delivering a data strategy?

Sponsors, business champions, data stewards, compliance teams, technology teams and partners are a few key roles for developing and delivering a data strategy. What is important is to start from the end - what is the business goal that you are trying to achieve or the problem you are trying to solve. Then, one can have all these stakeholders come together and contribute to building the data imperatives required to deliver the goals.

When there are multiple data and analytics initiatives how can we prioritize and align to ensure business impact?

The Digital Landscape is a great way to prioritize and align all your initiatives. On the x axis we have the skills required to deliver on the initiative and on the y axis we have the business impact that the initiative will have. Then a logical transversal of the landscape through these initiatives is the path for prioritization and alignment.