Successful companies know that achieving a competitive advantage in today’s market is largely a function of deploying better and more advanced analytics to a growing variety of data sets, says Johan Jurd, MD of InfoBuild, representing Information Builders in SA.
Analytics allows companies to gain insight into consumer and competitor behaviour, demand and supply trends, and operational performance. It also helps users make fact-based decisions on innovation, marketing, pricing, discounts, logistics and services.
The expansion of analytics is also driven by systematic, fully-automated, data collection and capture of behavioural data from multiple touch points. This data provides more evidence of what people do and why they do it compared to self-reported data that is typically collected by surveys and focus groups. Behavioural data allows users to see the actual consumer choices and behaviour.
As nearly every customer action and interaction today leaves minimal information in various systems and formats, it becomes more important to build a conceptual framework for enterprises to create an information management and analytics strategy.
Analytics is frequently characterised in terms of breadth (shown along the X axis). This indicates the variety of data types that can be analysed. Today’s businesses collect both structured and unstructured data.
These two source types must converge into one analytic system. More often than not, they are treated as fundamentally different and their management and analysis is organised in different groups and departments, and as such missing an opportunity to link the insights gathered.
The variety within each data type is enormous, but the idea is that unstructured data cannot be treated as a non-standard type of data outside of the BI and analytics system. It cannot be ignored by IT or by business analysts because it is considered to be outside the traditional data warehouse. It needs to be an integral part of the analytic strategy and process.
The depth of analytics (shown along the Y axis) is the dimension that characterises the increasing complexity of analytic techniques and methods that can be applied to the same data types. For example, the same data set can be analysed both descriptively and predictively, and the insights will differ greatly.
The descriptive analysis provides insight into what is happening, while the predictive analytics sheds light into what is likely to happen. The depth has to be correlated with the skills and talent available in the business. As more advanced techniques are used, the value extracted from the collected data increases exponentially.
The value of the present conceptual framework is to allow business intelligence and analytics to assess where their organisations stand with respect to those two dimensions and formulate both near and long-term strategies.
Users may ask why we choose the bell curve? As can be seen, the higher levels of analytics stack on top of the lower levels of analytics. Reporting is at the base because it is the starting point for all business analytics in every enterprise.
It also requires the least advanced skills for information consumptions (if done well) and is the foundation for pervasive distribution of information for decision-making. Predictive analytics and optimisation are at the top as they are highly specialised methods that can be performed by trained statisticians and mathematicians.
And yet, we want to convey an important message that if predictive analytics is embedded in the operational reporting, the average intelligence of the entire enterprise will increase.
Conversely, if analytics is implemented by statisticians and the findings are distributed in memos and power points, it will be in the tail of the bell curve (the graph will be inverted so that the types of analytics will stack vertically and not horizontally).
This will impact on the knowledge of a few rather than the actions of many. So the key to raising the analytic capabilities of the entire enterprise is to embed these higher methods into operational reports and dashboards so that all operational employees are empowered to make better fact-based decisions.
