Of all the changes that financial institutions are experiencing in current times, data-backed decision making has the potential to create the deepest impact. Advanced analytics tools give companies the power to grow the business, mitigate risks, enhance the customer experience and improve operational efficiency. But the road to becoming a data-driven organisation is often a slippery one — fraught with unmet goals and poor returns on investment.

When Gartner announced in 2011 that 70 to 80 per cent of corporate business intelligence (BI) projects would fail, it did create a lot of noise in the industry but also some quiet realisation of the challenges involved. Over these years, the data experience for most organisations has been a mix of excitement, satisfaction and frustration.

In my experience of working with financial institutions in the Middle East, I see the following common themes in the challenges faced in their BI journey:

• Absence of a well thought out enterprise-level vision and strategy — setting off without defining the objectives, integrating the efforts of different departments, setting the expectations or putting change management in place

• Unrealistic expectations — going with the hype, without understanding the limitations put by existing technologies or organisation culture

• Viewing it only as a technology challenge — ignoring the business and cultural aspects

• Flawed implementation — poor data quality, legacy systems that do not talk to new technologies, weak data governance and the inability to cut through data silos

• Inadequate skills — a growing gap in the demand and supply of relevant data skills plus the unavailability of required in-house techno-functional skills

• The absence of a robust business case — a mismatch between an organisation’s business needs and the capabilities of the solutions it implements or the inability to accommodate changing business priorities

Though most financial institutions have implemented data technologies, very few of them can be said to be data-ready at an enterprise level. We see organisations in various stages of data maturity. But only those who have got all the pieces in place — strategy, technologies, business plan, organisation, talent — can be said to have achieved optimal enterprise-wide data analytics capabilities.

Let me illustrate the complexities involved. A large Asian bank went through two data technology implementations that delivered only sub-optimal results. The bank had not reorganised itself for the new environment, had allowed data silos to exist, didn’t have the techno-functional expertise to carry out the change and lacked robust data management capabilities. The flawed implementations, apart from the costs involved, posed a risk to its growth, compliance and efficiency. The bank finally got it right after nearly three years when it implemented a comprehensive business intelligence and data strategy aligned to its corporate strategy, a business intelligence competency centre with cross-functional teams and techno-functional experts, a data governance policy framework, and processes to ensure integrity of data and reporting. Success also meant a blend of the right technology infrastructure, tools tailored to their needs and specialist talent.

In another instance, a leading Asian bank faced significant issues after implementing a new core banking system. The gaps in the system resulted in data inaccuracies and delays that exposed it to financial and regulatory risks. The bank then conducted a comprehensive post-implementation review of its new core banking system, identified and resolved the gaps, deployed an enterprise-wide data environment and introduced automated reporting. As a result, the bank can now produce its general ledger accurately and on time. Today the bank’s managers receive timely automated reports on their devices that help improve their decision-making capabilities.

Financial institutions in the Middle-East are making rapid progress in their data journey. Within those, credit card and insurance companies are taking the lead. Further, new entrants such as fintech firms, telcos and technology companies are changing the game for traditional players in this sector.

So, what are the ingredients of the secret sauce that makes an organisation a successful data-driven enterprise? I believe the journey must start with these four questions:

• Why: A clear understanding of why your organisation needs to embark on the data and analytics journey — will it aid in the realisation of the organisation’s strategy and business objectives?

• What: Knowing what data to use for which purpose and prioritising delivery

• How: Deploying the right organisation model, processes, technology architecture, solutions and capabilities

• Where: Build in-house or outsource, or go for a blend of the two

And, continue to re-examine the answers to the above questions in response to the changing business environment, organisation priorities, and emerging technologies and tools.

Very often, external specialists and service providers complement this journey through a flexible partnership model where the organisation can choose between a fully managed business intelligence service that aligns with its strategic goals or seek tactical assistance at various stages of its data journey.

International Data Corporation (IDC) expects the market for big data and business analytics to grow at a compound annual growth rate of 11.9 per cent till 2020, developing into a market of over US$210 billion. IDC calls out banking, followed by insurance, securities and investment services, among the biggest spenders.

But for the large outlays to pay off, organisations must back investments with strategic thinking and strong implementation capabilities.

— Sanjay Uppal is the CEO of Singapore-based execution consulting firm StraitsBridge Advisors and former CFO of. EmiratesNBD Banking Group