Data and analytics puzzle executives

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Business executives know the value of data and analytics (D&A), but are struggling with how to effectively implement and manage their resources according to a new survey.

Findings from the new KPMG Capital report, Going beyond the data: Achieving actionable insights from data and analytics, found 75% of executives find it difficult to make decisions around D&A, despite 99% considering it important to their business. The majority (85%) also conceded they were struggling to accurately analyse and interpret their existing data.

Anthony Coops, Head of Business Intelligence & Analytics at KPMG, told Corporate Risk & Insurance there are several reasons organisations are finding it difficult to make decisions around D&A, including a lack of understanding and certainty about what insights are possible.
“Data analytics, particularly advanced analytics, by its very nature focuses on uncovering patterns, trends and predictions as yet unknown, and this degree of uncertainty can be “unsettling” in a business environment where executives want to know tangible investment outcomes in advance of establishing investment cases,” Coops said.
“The other difficulty is in identifying what data to collect, selecting and implementing the right D&A solutions that integrate into existing systems/business models, and capturing data from all areas of the business to enable a true enterprise view.”
He added a perception that there are limitations to technology or data quality which can ‘block’ an analytics initiative from proceeding – rather than businesses taking a more pragmatic approach and starting with the information and technology they have.
“Sometimes, the difficulty lies in inability to drive a change agenda in the way in which decisions are made across the organisation (at operational, tactical and strategic levels) – moving away from intuition and “gut feel” towards a more evidence and facts-based approach,” he said.
Coops warns that failing to effectively manage data brings a number of risks – such as missed opportunity to gain insight into the market and customers.
“This could very well lead to the loss of customers to competition or the failure to capture margin or failure to cross sell or upsell opportunities. Examples of this include established and new entrants with contemporary business models (e.g. Google Wallet, Paypal) taking market share away from traditional banking providers,” he explained.
There is also the potential of financial loss from fraudulent activity across core processes such as procurement and payroll and along with the risk of reputational and financial risks that are a flow on effect of disclosing inaccurate information to external parties such as industry regulators and financial markets.
“In addition, if strategic, tactical and operational decisions are made on the basis of reporting and analytics produced from information that is stale, of poor quality or not “fit for purpose”, you can be sure that the investment of often scarce resources, both financial or non-financial, will be very misguided,” Coops added.
When it comes to finding the right solution for tackling D&A, Coops recommends companies take a holistic approach. Companies should ensure they have a coherent set of objectives for analytics that “align with the overall business strategy, coupled with the company’s appetite for innovation, risk, change and investment”.  A good place to start looking for solutions is organisational priorities, strategic imperatives and burning issues Coops said.
When looking for a solution for managing D&A, Coops said it is important to consider the following:
  • Key objectives – Firstly identify what the solution is meant to help achieve drives the types of outputs required.  This in turn, drives the analytics processes to be enabled and data sources to be captured.
  • Processes – Think about the way in which analytics can be embedded into the organisation’s business and decision making processes and how that meshes with given management culture and behavioural norms.
  • Other components – Go beyond looking purely at technology and data requirements and think about people and process components of an analytics framework
  • Working outside the box – There’s something to be said for working outside of the organisation’s standard project delivery processes in order to rapidly deliver value in a flexible and agile approach which can adapt to new insights uncovered progressively (e.g. pilot, proof of concept projects).
  • Governance - The need to implement an ongoing program of data governance and management to improve the quality and management of information across the organisation.
  • Unusual structures - The unique team structure and skill-sets required for an analytics initiative including active involvement from business process / data SMEs and analytics consumers (e.g. decision makers).

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