Todayâs CIO has more data available than ever before. There is an opportunity for potential big improvements in decision-making outcomes, it carries huge complexity and responsibility in getting it right.
Many have already got it wrong and this is largely in part down to organisational culture. At the centre of creating a successful analytics strategy is building a data-driven culture.
According to a report by Gartner more than 35% of the top 5,000 global companies will fail to make use of the insight driven from their data. In another report by Eckerson, just 36% of the respondents gave their BI program a grade of âExcellentâ or âGoodâ.
With the wealth of data already available in the world and the promise that it will continue to grow at an exponential rate, it seems inevitable that organisations attempt to leverage this resource to its fullest to improve their decision-making capabilities.
Before we move forward, itâs important to state that underpinning the success of these steps is to ensure all employees who have a direct involvement with the data or the insight generated are able to contribute. This point is highlighted in a case study of Warby Parker who illustrate the importance of utilising self-service technologies that help all users meet their own data needs, which, according to Carl Anderson, the director of Data Science, is essential in realising a data-driven culture.
Set Realistic Goals
I suppose this step is generic and best practice across all aspects of an organisation. However, I felt it needed to be mentioned because there are a number of examples available where decision-makers have become disillusioned with their analytics program due to it not delivering what they had expected.
Therefore, CIOâs should take the time to prepare in-depth research into their organisation; I recommend they look at current and future challenges facing their organisation and tailor their analytics strategy appropriately around solving these.
During this process, it is important to have a full understanding of the data sources currently used for analysis and reporting by the organisation as well as considering the external data sources available to the organisation that are not yet utilised.
By performing extensive research and gaining understanding on the data sources available to the organisation, it will be easier for CIOâs to set realistic and clear goals that address the challenges facing the business. Though there is still work to be done addressing how the analytics strategy will go about achieving these goals, itâs at this point where CIOâs need to get creative with the data available to them.
For example, big data has brought with it a wealth of unstructured data and many analysts believe that tapping into this unstructured data is paramount to obtaining a competitive advantage in the years to come. However it appears to be something that most will not realise any time soon as according to recent studies estimate that only around 0.5% percentage of unstructured data is analysed in the world.
Build the Right Infrastructure
Once the plan has been formulated, the next step for CIOâs is to ensure that their organisationâs IT infrastructure is aligned with the strategy so that the set goals can be achieved.
There is no universal âone way works for allâ solution on building the right infrastructure; the most important factor to consider is whether the IT infrastructure can work according to the devised strategy.
A key requirement and expectation underpinning all good, modern infrastructures is the capability to integrate all of the data sources in the organisation into one central repository. The benefit being that by combining all of the data sources it provides users with a fully holistic view of the entire organisation.
For example, in a data environment where all of the organisationâs data is stored in silo, analysts may identify a trend or correlation in one data source but not have the full perspective afforded if the data were unified, i.e. what can our other data sources tell us about what has contributed to this correlation?
Legacy technologies that are now obsolete should be replaced in favour of more modern approaches to processing, storing and analysing data â one example are those technologies built on search-engine technology, as cited by Gartner.
Enable Front-Line Employees and Other Business Users
Imperative to succeeding now is ensuring that front-line employees (those whose job roles can directly benefit by having access to data) and other business users (managers, key business executives, etc.) are capable of self-serving their own data needs.
CIOâs should look to acquire a solution built specifically for self-service analysis over large-volumes of data and capable of seamless integration with their IT infrastructure.
A full analysis of employee skill-set and mind-set should be undertaken to determine whether certain employees need training in particular areas to bolster their knowledge or simply need to adapt their mind-set to a more analytical one.
Whilst it is essential that the front-line employees and other business users are given access to self-service analysis, inherently they will likely be âless-technical usersâ. Therefore ensuring they have the right access to training and other learning tools is vital to guarantee that they donât become frustrated or disheartened.
By investing in employee development in these areas now, it will save time and money further down the line, removing an over reliance on both internal and external IT experts.