Challenges of bolstering data analytics capabilities
At a time when focus on revenue and performance is under significant scrutiny, companies are increasingly reviewing the opportunity costs of internal verses outsourced data analytics capabilities, according to the Australian Head of Deloitte Analytics, Anthony Viel.
Business reporting is vitally important to the organisation however it is not the source of competitive advantage in the modern world. Mr Viel believes that many organisations that fail to utilise data and analytics in the business decision making and planning process risk losing market share and competitive advantage.
The pace of growth and the sophistication of the modern business leader means there is a greater need to capitalise on the ever-increasing volumes of data available to them.
Companies’ ability to source, capture and store large volumes of data and the processing power of computers is growing at an exponential rate. To keep pace with this growth and the demands of business, the discipline of data analytics is also developing in sophistication and breadth of application.
As organisations better exploit these factors across their entire business, gut-feel, instinct, long held traditions, biases and prejudices will be dismissed from, or at best be a co-contributor, to the decision-making process.
The cost of an internal analytics capacity
“However, when it comes to sourcing, optimising, analysing and interpreting the massive volume of data and then applying the learning in order to predict, forecast and optimise business strategy and processes, access to this analysis from internal capabilities can often be inhibited,” he said.
“Issues such as the timeliness of analysis, available competnent resources, having a project specific focus and immediate accountability can be overlooked. Yet this information is often a business priority and delays result in a significant impact on the bottom line,” he said.
Mr Viel said that most organisations charge head long into thinking building a capability is the shortest path to success.
Notwithstanding that good governance principles and the need for timely business analytics and reporting dictate that an in-house build of analysis tools and data repositories is necessary, rarely do organisations understand the actual cost of the in-house data analytics build.
There are three primary cost issues to consider:
- The cost of waiting is undoubtedly the most underestimated and neglected cost of the in-house build of analytics capability. Depending on the organisation’s system maturity and the complexity of the operations of the organisation, it is not atypical that the time to go from decision to return on data analytic investment can range from 6 months to 3 years. A lot of opportunity passes the organisation in this time.
“Multiply the per annum benefit articulated in the in-house build business case by three. This is a good proxy for the cost of waiting,” says Mr Viel.
- The financial cost of infrastructure, software and to recruit and train analysts can be significant and is always underestimated. Three key phases are firstly; the infrastructure and application selection and sourcing; the build implementation and user acceptance and finally, the recruitment and training of capability.
"When was the last time one of your IT projects ran on time, on budget and delivered everything originally promised?” asked Mr Viel. “Then consider how difficult it is to recruit and retain first class data analytic resource, particularly when the entire data analytic skill set is probably the most popular and scarce resource in the global human capital market today."
- The ‘white elephant’ cost is the changing competitive landscape, globalisation, technology developments and the sophistication and increasing expectations of customers continue to challenge the modern organisation. These challenges can change significantly the analytic requirements and or priorities of the business at the time of deciding that a data analytic capability should be built (or acquired) and that are actually delivered at a later time. However, it is not unusual for the requirements of the business to move on (usually complexity) by the time the capability has been delivered. Further exacerbating this shortfall is the unwillingness of the business to ‘fully-invest’ in the data analytic capability. The first day of delivery may feel a little like groundhog day for business yearning for the data analytic fuelled business results as the gap between in-house capability and business requirements has not narrowed, despite investment.
”Tomorrow there will be more competitors, fewer resources, more informed customers and more data available but how many organisations successfully predict the implications of these one to three years in advance? The cost of not knowing and not being adaptable and speed to market need to be taken into account,” said Mr Viel.
What can an organisation do?
Mr Viel says management needs to “Pick the right fight.” “Don’t try and move from zero to hero in one move, you will be disappointed,” he advised. However, some more successful organisations have shown us some key learnings:
- Eat the elephant one bite at a time: “There is a wealth of opportunities for return utilising data analytics in the modern organisation. Focus on the low hanging fruit, succeed and then move on,” he said. “One of the most difficult things to do when in charge of developing a data analytics capability is choosing where to start. I recommend setting a 3-5 year vision and then choose just one area of the business where an immediate challenge exists and attack this area in a fashion consistent with the vision.
- Carpe diem: “Seize the day and choose an area of the business where data analytics can have a short timeframe to payback. Long term promises on the back of a large investment can scar the organisation for a management generation.”
- Toe in the water: Prototype the proposed data analytics capability before the organisation has to make the investment. This not only determines what analytic sophistication and data you need to start achieving the returns, but most importantly highlights what you don’t need in terms of infrastructure, analytic tools, human resources and data sources.
- Park the ego: As the owner of the data analytics capability, you can leverage data analytic capability intellectual property or infrastructure in a low risk (or risk free) environment, directly or in parallel to an in-house initiative. It can help you to achieve short term returns, learning or raise the profile of data analytics within your organisation. However, not using the benefits means you are depriving the organisations stakeholders of profit of highly valuable information. If you as owner of the data analytics are aware, or have experience, of the scale of the benefits available through the smart application of data analytics, has serious liability implications.
“Data analytics is arguably now, and will definitely be in the future, the last source of competitive advantage,” said Viel. “The slower an organisation deploys data analytics at the front line - the further the organisation will fall behind.”