Use data to overcome junk status
Curiously, I have observed the mixed reactions to the credit-rating changes of our beloved country. The responses have ranged from full-blown denial to full-blown panic. However, in reading various opinion pieces on the subject, it seems like uncertainty is probably the norm.
I must be honest and say I am on the fence as to whether the so-called junk status is in fact as bad as the initial knee-jerk reaction seemed to indicate. Reflecting on history, I'm optimistic that economic players can weather the storm if they heed the lessons learnt.
Companies that survived the 2008 economic crisis varied in size and tenure, but they all seemed to have one thing in common: data analytics was a cornerstone of their management culture during that time. They had solid processes in place to extract insights from their available data, which informed their operational and strategic direction, sustaining them through to economic recovery.
Testament to this fact is that increasing use of information through analytics consistently ranked in Gartner's Top 10 Business and Technology Priorities since 2009.Furthermore, regulatory bodies, especially in the financial services industry, observed this phenomenon and have since started to adopt fundamental data management best practices into regulation, such as Basel's BCBS239 (risk data aggregation and reporting), to not only promote information-driven decision-making, but also to ensure the decisions are made based on sound quality information, which can only be achieved through effective data analytical processes.
Considering this, what lessons can be carried from the last decade into the next when it comes to tougher economic times and junk status ratings? Some include:
- Ensure to empower management, on all levels, with true insights to sustain the company through the sub-investment grade era, or to increase competitiveness, emerging from this storm stronger than before.
- True insights mean the insights must be fundamentally correct. Therefore, data management practices, like data governance and data quality management, become critical success factors for all analytics practices in place.
- Delivering high-quality information with embedded potential insights offers no value if all levels of decision-makers are not truly empowered to access and interpret the insights effectively and, of course, timely. Successful empowerment is dependent on having the right toolset for the various users and use-cases, as well as facilitating effective access and interpretation of the information using best practices from meta-data management and/or search analytics capabilities.
- It is important to remember the investment in true empowerment will result in sunken costs, if an information-driven culture is not fostered intentionally. In fact, building an information or data-centric company is becoming critical for those wanting to develop a strong competitive advantage today. The most effective strategy to achieve this culture change is buy-in and championship from the highest levels of management, down.
Inevitably, some tech trend questions will arise as companies consider these four lessons while formulating their data management strategy for the future. Questions like: "Should we invest in big data analytics or data science, or perhaps both?" and "Should we convert our capex to opex and migrate everything into the cloud?"
Answers to these questions do not exist in any available textbook or whitepaper, as the answers are relative to organisational and industry nuances. Use the four lessons above as a barometer, as well as basic common sense, in assessing strategic decisions going forward. Question if there are specific use-cases that decision-makers face, which can only be empowered by big data or data science approaches. Ask things like: "Are we going to lose significant competitive ground if we don't adopt new analytics strategies now? Is there a financial gain from adopting analytic strategies like cloud, which significantly improves the bottom line of the business?"
However, these new tech trends can essentially be considered 'blue ocean strategies'; therefore, one should first assess the maturity of a company's fundamental capabilities, and cover the gaps. Areas like data quality, data governance, management information systems and corporate performance management capabilities (such as reporting, dashboarding and score-carding), as well as historical and descriptive analytics, should be examined as the highest priority if a company wants to sustain its current competitive standing.
The next level of maturity or sophistication in analytics that provides predictive insights is ultimately dependent on these fundamental capabilities. True predictive insights can only be trusted and utilised if the data in each model is relevant to the context, and of reliable quality. This does not happen by waving a magic technology flag at the data. High quality data is only derived through management accepting the responsibility of managing data as a true asset, taking ownership of the information from point of capture through to exploitation in analytical models.
Ultimately, the "junk" status storm can only be survived if companies can leverage the data within their reach - as their wave - and the strength of their fundamental data management and analytic capabilities will determine how well they stay afloat during this difficult time. Investment in the latest tech trends, however, could spur them to world-champion surfers, but only if they're confident they will stay afloat.