Attempting to find purposeful insights in data could be a futile exercise unless you look beyond the siloes.
With the mainstreaming of advanced data analytics technologies, companies today can risk becoming too dependent on the outputs they receive from the analytics tools, which could serve biased results unless solid data analytics models are applied to the way in which the data is interrogated.
While data is your friend, and the only valid way for organisations to strategise based on fact, data analytics tools can only deliver the outputs they have been asked for. If the pool of data being analysed is too limited, or there is no end objective or purpose for using the results after the scientific methods have been applied to the data, then the whole exercise is virtually futile.
It is seldom enough to drill down into a limited data repository and base broad strategic decisions on the findings. In effect, this would be like a novelty manufacturer assessing only the pre-festive season sales and concluding that Christmas trees are a perennial best-seller. Common sense tells us this will not be the case, and that Christmas trees won’t sell at all in January. But in the case of more complex products and services, trends and markets are not as easy to predict. This is where analytics comes in. Crucially, analytics must look beyond specific domain insights and seek a broader view for a more objective insight.
Comparisons and correlations
A factory may deploy analytics to determine which products to focus on to increase profits, for example. But where the questioning is too narrow, the results will not support strategic growth goals. The company must qualify and complement the questioning with comparatives. It is not enough to assess which products are the biggest sellers – the factory also needs to determine what products are manufactured at the lowest cost, and which deliver the highest return. By bringing together more components and correlating the data on the lowest cost products, highest return products and top sellers, the factory is positioned to make better strategic decisions.
In South Africa, many companies do not approach analytics in this way. They have a set of specific insights they want, and once they find them, they stop there. In this siloed approach, the results are not correlated against a broader pool of data for more objective outcomes. This may be due in part to factors such as the time and cost required for ongoing comparison and correlation, but it is also due to a lack of maturity in the market.
In mature organisations, data sciences are applied to all possible angles/queues and information resources to produce insights to monetise or franchise the data. It is not just a case of finding unknown trends and insights – the discovery has to be purposeful as well.