There is no denying the fact that businesses are continuously evolving to become more digital. And as part of this digital transformation, it has become very clear to many business owners and decision makers that data and analytics will play a key role in the success of a business across any industry. In this post we investigate two of Gartner’s recent predictions about data and analytics.
Data is a business asset that today, if analysed correctly by applying the right analytics, can offer a business not only improved operational efficiency, but a strong competitive advantage.
Gartner’s predictions
I was recently reminded of this fact when reading a very interesting predictions piece from Gartner – 100 Data and Analytics Predictions Through 2020 – that presents some fascinating predictions about advanced analytics. Two of those related to the application of analytics that stood out for me are:
- By 2018, over half of large organisations across the globe will use advanced analytics and proprietary algorithms to compete – which will in turn cause disruption across industries.
- By 2018, the concept of decision optimisation will not be considered a niche business discipline. Rather, it will become a best practice method that organisations who want to lead will utilise to manage a rage of various complex decisions a business needs to make.
So, I have to ask myself whether these predictions will come to fruition to the extent predicted. And if so, will it be within the two year time frame as Gartner’s analysts predicted?
Organisational readiness
I certainly agree that these sentiments have potential, especially given the opportunity that advanced analytics offers a business that is truly digitally transformed. However, my experience shows that many CEOs/CIOs and other key decision makers in the C-suite often find advanced analytics overwhelming. While it is relatively easy to devise an analytical model – provided you fit the right model for the problem at hand, and you have suitable data sets – many organisations battle to “operationalise” such models, especially with regards to taking action based on the model outcomes.
Timeframes
For some agile and fast-changing businesses, the two years until 2018 seem like an eternity – but those are not the organisations I’m concerned about. I’m worried about those older style organisations where every little change has to be debated on all levels first, before it is implemented through an extensive change management program. In such an organisation, a two year window flashes by in a blink, without much significant change being implemented. So the question is – do more or less than half of the large organisations out there fall into this category?
While I certainly hope it takes place, I believe it may take longer than expected for more than half of organisations to reach such a state. The main reason for this sentiment is that I think organisations underestimate the complexity of implementation, operationalisation and on-going maintenance of analytical models.
Technology vendors
A second fact is related to the technology vendors who are offering advanced analytics solutions. These vendors promise great claims but often lack transparency as to the range of skills required to use advanced analytics optimally and – importantly – to produce actionable results. (Getting the organisation to act meaningfully on the results is another story as well, as discussed above.) Although the tool sets are coming out with more user-friendly and easier to navigate user interfaces, it doesn’t make the skills and experience required to drive them properly and make sense of the outputs any less.
Consultants’ role
As experts in the industry, it is our duty to ensure that this complexity is properly understood, in order to play our role in helping these predictions come true realistically. However, it is also about ensuring that advanced analytics is fully understood by businesses, before being invested in – to avoid empty promises or a disappointed and disillusioned business owner down the line. The complexity lies not in the tool or the analytics model itself – well, it shouldn’t be… The complexity lies in understanding the mathematics and statistics behind the implementation of the model’s outcomes, and the implications and efforts required to get the business to adopt the model outcomes and act appropriately on it.
Concluding remarks
The collective predictions made around data and analytics is no doubt great for this space, and make for very interesting reading, but it does also mean that several business transformations are needed and with this, many challenges may lie ahead for CIOs and IT leaders within a business, as they work to get this right. There is no doubt in my mind that advanced analytics should be embraced and included in any suitable business digital model or strategy – but of course like most other aspects related to data, information, knowledge or insight, it is the extent of the organisational uptake that will deem it a success or not.
