Consider these steps when implementing data and analytics capability in your company

Data has become a major business asset. Nowadays, companies across every industry and sector are leveraging data to understand customers better, create value at every interaction and gain a sustainable competitive advantage.

This is achieved by deploying data and analytics to translate complex data into meaningful insights that enable more purposeful customer engagements and business decision-making processes. 

To unlock the value that data provides, organisations must focus on getting certain elements right to enable a successful and scalable implementation of a data analytics capability. Lasath Punyadeera, Standard Bank's head of personalisation & profit science, consumer and high net worth markets, provides the following considerations:

1. Getting data basics right 

Democratised access to data, secure data storage, compliant data environments and stable data platforms are fundamental aspects in setting up a data environment that can support and grow analytical capabilities. If getting the right data is a struggle or if analytical capabilities are starved of data, there are likely to be fewer facts and more fiction informing data-driven decisions.

2. Technology and software set-up 

It is imperative to always build data and technology stacks with scale and future requirements in mind. An important point to note is that allowing full flexibility on the choice of technology, software and programming languages can translate into significant integration complexities down the line. Organisations need to make the right choices up front, consider the use of interoperable technologies, set up coding standards and govern the choice of programming languages.

3. Organisational readiness and buy-in 

Organisational readiness, buy-in and sponsorship remain key to setting up a successful data and analytics capability. This is usually the first and most important step to get right for any data and analytics capability to attract investment, grow, scale and survive in an organisation. 

4. Data health 

Data quality, availability, timeliness, completeness, consistency, accuracy and reliability are key inputs in designing the right data environment to complement analytical capabilities. Early focus on getting the health of data right will go a long way to accelerate intended analytical solutions over time. This will avoid wasting specialist analytical skills on elementary data-cleaning exercises.

5. Building a true data-driven culture 

The biggest hurdle to building a data culture is less technical and more cultural in nature. It’s not the lack of data specialists, software or technology that derails transitions, but the mindset of people in the system — who at times may be inadvertently working against the system. From leadership that sponsors and encourages, to managers that can comprehend and value the language of data, to data teams that are passionate, competent, and creative in producing data and analytics. All these gears need to engage smoothly for a data-driven culture to take shape and flourish.

Once a true data culture has been built, the organisation needs to avoid the temptation of second-guessing data-driven decisions — if this dominates in an organisation, the data-driven culture is likely to die a slow death over time.

6. Improve data literacy across the organisation

Work needs to be done, continuously, to improve data literacy across the organisation. The basics and value of data in solving business problems needs to be understood. This will help people realise the value of data and will naturally generate demand for the right data from people closest to the problem. The ideal is always to showcase the inherent value of data in the decision-making processes and establish a “pull” adoption from people vs a “push” adoption to the people.

7. Creating purpose and aligning to organisational goals 

The purpose of data and analytics needs to be intricately aligned to problems that need to be solved as well as organisational goals. The workbook of data and analytics capabilities need to be informed by the user’s problems, and strategic goals and performance outcomes of the organisation. This will ensure the analytical teams remain focused on things that matter. In addition, the data and analytics teams need to understand the bigger picture and the way their contribution translates into the organisation achieving its strategic imperatives and delivering on commercial performance.

8. Integrated ways of working 

Analytical capabilities are often ring-fenced and centralised as a centre of excellence for various reasons. However, this centralisation should not translate into the unintended consequence of creating physical and psychological barriers for integration.  It is important to accept that people closest to the problem have a better understanding of the problems they need help solving. Deliberate steps need to be taken to create permeable boundaries between business and analytics, complemented with fluidity to allow analytical teams to rotate in business and business teams to understand analytics. 

9. Analytical skills and experience 

Depending on the type of analytical requirement, from the outset the right skills which are fit for purpose need to be mapped out. As a first step, this necessitates a skills assessment to understand the current skill set and potential gaps relative to the problems to be solved. 

Work towards getting the required skill set blended with new hires from outside the organisation. It is important to give equal opportunities to individuals in the organisation: parachuting individuals in without giving opportunities to existing teams will undoubtedly destroy the team morale and this can have disastrous outcomes. Not all the skill sets need to be sourced upfront. It is prudent to think big but start small and then scale up based on business demands and proof of value. 

10. Managers who speak the data language and drive adoption 

Strong analytical teams can fail miserably in the wrong management hands. These management failures are not as obvious, as the blame may be transferred directly to the team, which is why organisations need to pay close attention to these dynamics. Managers of data teams need to speak and relate to the language of data, comprehend analytical methods, and be able to challenge and guide data and analytics teams. 

Managers need to have a passion for driving adoption of data and analytics to ensure their teams are working on meaningful use cases, translating business problems into the language of data and the data into actionable insights and tangible benefits.

11. Staying true to the business case  

Data and analytical capabilities are established to solve business problems with the expectation of beyond business-as-usual benefits for both customer and commercial objectives. Analytical capabilities inherently have a high commercial purpose for their existence. To this extent, it is important to have commercialisation built-in by design and intent and should never be attained by accident or as an afterthought. This is a key step to staying true to the initial business case, gaining credibility from the organisation, sustaining current analytical capabilities and securing additional funds for future expansions. 

12. Accurate measurement and value attribution 

A robust measurement framework, agreed and approved by all relevant stakeholders, that can accurately measure, and attribute value delivered through analytics, remains imperative to showcase the value of data and analytics. This will enable organisations to understand the real return on investment of the analytical capabilities, which remains a key input into future investment decisions.

13. Experimentation and innovation 

Always create space for curiosity and direct this into innovative analytical solutions for solving real business problems. Analytics can uncover many ideas; however, these need to be tested through scientific experimentation and proof of concepts to identify practical solutions which are scalable.

14. Training, upskilling, and networking 

Analytical methods, software and technologies are continuously evolving. To maintain analytical capabilities fit-for-purpose, continuous training and upskilling is required. This will create analytical capabilities that can survive the test of time. It is best to upskill on new analytical methods and technology as close to the time of application as possible. It is also important to allow analytical teams to network with industry experts and to participate in industry forums, competitions and challenges. This will ensure the teams are always at the forefront of advancements in the analytical domain.

This article was paid for by Standard Bank.