Few would argue that a sound data analytics strategy is superfluous to success in today’s economy. It doesn’t matter what industry you are in or how digitally mature you are, the benefits of data-driven decision-making are clear. And you don’t need to operate a platform business model to dramatically improve cost efficiencies or drive new revenues. Nor do you need to undergo radical transformation in how data analytics drives decision-making across all your departments. 

In fact, we have seen that some of the most successful data projects have been achieved through incremental steps, with a focus on securing early wins that help introduce teams to a new way of working. 

Data champions are also more likely to: 

  • Overcome hurdles such as data siloes; 
  • Achieve the right balance of skills and expertise in their data teamsand 
  • Invest in adequate technology on an adapted infrastructure. 

This article will explore three key characteristics for ensuring business return on your data strategy. 

Successful data analytics programmes are characterized by top-down adoption and sound technological infrastructure 

 

These data champions are more likely to benefit from a top-down approach, with a management team that actively integrates data into its business strategy, consistently across all organisational activities. Top-down implementation is essential in ensuring smooth transition to new tools, in showcasing the benefits to teams and in securing enough investment in regular training sessions to achieve the desired results. 

While the digital infrastructure you invest in is by no means a solution in itself (no amount of technology investment will compensate for a well-run data management function), it certainly helps to identify your organisation’s biggest opportunities for delivering ROI with data projects. 

Often this begins with hiring a reliable third-party service provider or a business-savvy CTO who provides the experience necessary to differentiate between projects that require advanced artificial intelligence or machine learning, or those that need workflow-focused solutions.  

Data teams need talents in analysis, wrangling, structuring, communication and subject matter expertise 

 

Overcoming this challenge of business-oriented data projects is no mean feat. C-suite support is required to ensure comprehensive and company-wide cooperation to remove the threat of data siloes and to enable tangible measurement of data initiatives. 

At the team-level, employees need sufficient understanding of the data tools, processing, structuring and analysis required of them. Data projects also need employees with additional skills such as communication (for instance, in the form of data visualization) and subject matter expertise. This point is critical for data projects to be executed within the framework of a business goal and to generate value. 

Just like an internet user knows how to formulate his question on Google to get the results he wants, so too must data teams know how to formulate precise queries that are detailed enough to identify opportunities for improving business value. 

Data champions are adoption-focused 

 

Ask any seasoned analyst where most data programmes fail and they’ll likely respond with ‘at management level’. Too often, teams try and tackle challenges using data to solve problems, without regard to a higher business strategy. The C-Suite must be involved in driving the direction of the analytics programme to ensure that it directly supports commercial goals and provides insights that can be actioned by different departments. 

This is where the value of incremental improvements comes into play. Multiple small improvements which are made possible by data analytics can amount to significant savings and efficiencies. Our preferred strategy is an agile approach that identifies numerous small data projects: the modest investment each requires means companies can drop those which don’t generate profitable insight and focus on those which do. It’s also easier to integrate insights progressively to form a solid data culture in this way, as teams can directly see the benefits of the data programme. 

Just like digital transformation is not a goal in itself but rather a means to an end, data analytics must also be considered in light of a higher purpose. You can have the best data insights in the world but if your teams don’t adopt them consistently across your organisation, you won’t be getting the best value for your data analytics strategy. 

About the Author

After completing his training in M&A at Skadden Arps, Anastasios Papadopoulos founded Integrated Management Systems (IMS) in 2016 and played a key strategic role in positioning the company as one of the leading Digital Transformation Agencies in Hong Kong.

He brings with him his experience in M&A and Tech, and also founded IMS Digital Ventures: the innovation, incubation, and investment arm of IMS and Hong Kong’s first corporate venturing firm that launches and invests in disruptive businesses with Asia’s largest corporations.

Anastasios Papadopoulos read Law in France and in the UK and holds a Management degree from HEC Paris.

Connect with Anastasios Papadopoulos on LinkedIn.

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