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How to create a roadmap for data analytics

How to create a roadmap for data analytics
(Image credit: Pixabay)

Enterprises of all sizes, all over the world, have now recognized that data is an integral part of their business that cannot be ignored. While each enterprise may be at a different stage of their personal data journey - be it reducing operational costs or pursuing more sophisticated end goals, such as enhancing the customer experience - there is simply no turning back from this path. 

In fact, businesses are at the stage where data has the power to define and drive their organisations overall strategy. The findings from a recent study by Infosys revealed that more than eighty-five percent of organisations globally have an enterprise-wide data analytics strategy already in place. 

This high percentage is not surprising. However, the story does not end with just having a strategy. There are numerous other angles that enterprises must consider and act on before we can deem a data journey as successful.

About the author

Gaurav Bhandari is AVP and Head of Data & Analytics Consulting at Infosys.

Developing a data strategy

First, enterprises need a calculated strategy which covers multiple facets. Second, the real life implementation of the strategy must be seamlessly carried out - and this is where the challenge lies for all enterprises. 

Consider having to create a comprehensive and effective strategy for your company. Data strategy is no longer about simply identifying key metrics and KPIs, developing management roles or creating operational reports, or working on technology upgrades. Rather, its reach extends to pretty much all corners of the business. 

In short, data strategy is so tightly integrated with business today, that it is in the driver’s seat, which is a momentous shift from more traditional approaches of the past.

What are the characteristics of a good, strong data strategy?

Creating a good, strong data strategy begins with ensuring complete alignment with the organisation strategy. The data strategy must be closely aligned to the organisational goal, be it around driving growth or increasing profitability or managing risk or transforming business models

Not only that, but the data strategy must be nimble and flexible, allowing periodic reviews and updates to keep pace with wider changes in the business and market. The data strategy should be able to drive innovation, creating a faster, better and more scalable approach.  

A strong data strategy must be built in a bi-directional manner so that it can enable tracking of current performance using business intelligence to provide helpful pointers for the future. This approach is only possible if organisations choose to adopt a multi-pronged data strategy that encompasses people, technology, governance, security and compliance. Importantly, organisations must also choose to adopt an appropriate operating model.

Taking a holistic approach to data

A holistic approach includes developing a defined vision, having a clear structure around the team and factoring in the current skill set of the team. This is in addition to considering what the enterprise can reasonably anticipate in the future and identifying mechanisms to successfully drive the change across the organisation.

The technology component involves having a distinct vision, assessing the existing solution landscape, all the while being cognizant of the latest technological trends and arriving at a path that fits well with overall organisational goals and the technology vision. 

Governance, security, and compliance are other critical aspects of a good data strategy. Integrity, hygiene and ownership of data, plus relevant analytics on the data to determine the Return On Investment on data strategy, are all essential steps which cannot be forgotten. We cannot overstate the importance of security. 

Adherence to compliance has assumed significance with various regulations in play all over the world, such as GDPR in Europe and new data privacy laws in California and Brazil for example. 

In essence, the data strategy must define a value framework and have a reliable mechanism to track the returns to justify the investments made. About fifty percent of respondents to our survey agreed that having a clear strategy chalked out in advance is essential to ensuring an execution that is effective in practice and goes off without any hiccups.

Identifying the best strategy is essentially pointless if the execution falters

Many obstacles have the power to prevent the flawless execution of a data strategy. Copious challenges in the technology arena can arise in various forms, for example: having the knowledge to choose the right analytics tools, lack of availability of people with the right skill set, upskilling, reskilling and training the workforce with the necessary skills for the world of tomorrow and so on. Most of the challenges articulated by respondents to the Infosys survey arose in the execution phase of a data strategy.

While these challenges may appear daunting in the first instance, they can be addressed with careful planning and preparation. Being prepared and equipped for multiple geographies, multiple locations, multiple vendors, talent acquisition and good quality training are just some of the numerous possible ways companies can begin working towards smooth execution of their digital strategy.

 

Gaurav Bhandari, AVP and Head of Data & Analytics Consulting at Infosys.