Of late, business enterprises have woken up to the potential of data. In organisations, executives are investing time into analytics projects. Business analytics is a powerful tool, but only if an organisation embraces the type that is the best suited to its requirements.
The Analytics Offerings
Analytics come in four distinct offerings, with each having its own parameters of deployment and each level supporting the next. Descriptive, diagnostic, predictive, and prescriptive are the four analytical offerings powering business enterprises today.
The base of the analytics pyramid is descriptive analytics. Descriptive analytics analyses what happened and allow analysis of historical data to identify an organisation’s end goals. For instance, a descriptive report for a food joint could show how many dishes were sold last month and the revenue generated from different markets.
Above the descriptive analytics lies the diagnostic analytics, which reports the cause of an occurrence to pinpoint what factors drive positive and negative performance. If the food joint’s descriptive report shows that sales are down from a particular store, a diagnostic report will pinpoint the cause why it happened.
Predictive analytics lies on the top of descriptive and diagnostic analytics. Predictive analytics reports what could happen in future, based on the historical performance. If the food joint wants to increase revenue, its predictive report could show the markets where it can increase its marketing spends to increase revenue.
Prescriptive analytics, lies on the top of the pyramid to report on what should happen. Predicative analytics deploys artificial intelligence and machine learning to mine historical data to take future decisions. Prescriptive analytics could tell the food joint’s marketers exactly which price points to push in their latest new pizza offering.
The Analytics Business
It is often seen that companies often prioritize a single tier of the analytics pyramid without working on the crucial primary levels.
The analytics business process starts with sanitising the operational data. Only when the operational data is properly structured and organized the next more advanced analytics steps can be chalked out. Companies that fail at this step are often saddled with huge tech costs, and software bottlenecks which create impediments to its full potential and weak overall insights.
After mastering level 1 i.e. descriptive analytics, companies can explore whether the higher analytics levels are even necessary. Many companies reach to business intelligence with small data analysis unlike the giants which study the high-velocity and high-volume data for future insights. The key lies in being realistic about the businesses needs and capabilities.
Working on the Analytics Process
For business houses to develop their analytics agenda, the process starts with asking the right questions to fully understand their needs and wants, budgets and outlays. For starts, companies can begin with these important inquiries:
1. Data Sources
The first step of any analytics agenda is the access to clean data that is consistent and reliable, because a company cannot even reach to diagnostic reporting if it depends on conflicting data sources. Step one is data cleaning to make it consistent and reliable. Companies must decide on a comprehensive information source for its data retrieval, without that its insights will be inaccurate and inevitably incomplete.
2. Data Professionals
When companies have clean data the next step is the tech resources who will make the data speak insights. Data analysts are experts trained in statistical analysis and data modeling techniques. It is imperative to have data professionals for keeping data organized and actionable for interpreting insights. Until the business enterprise has a data analyst on board, it risks drawing false conclusions from the data sources. It is productive and efficient to make someone specifically responsible for data analytics in an organisation.
3. Analytics Tools & Software’s
Having good data for analytics and the essential professional expert on board, the next step is harnessing the best of Analytics tools and software’s available. Modeling tools include R, SAS, and Python and so on; it is on the business to choose which one will suit the best to them. Even for dynamic reporting, an analyst may have Excel, Tableau, or Qlik software, choosing the one that gives the best outputs is a critical decision.
4. Analytics Cost
Cost is an essential decision maker which influences all the above points. High-level analytics require high-level data which may increase the project costs. Data and technology deployed into predictive and prescriptive analytics are both expensive. Without the right costs outlays, the ROI on high-level analytics may prove to be costly. Thus, companies must weigh do costs benefits analysis before choosing the analytics methods.
The potential of analytics is limitless, tempting organisations to deploy this magic wand to push projects forward. However, keeping costs and organisational strategies in mind, the best approach is to exercise caution and prioritize planning. The more business enterprises calibrate analytics up front, the more valuable analytical insights they will gain in the long run.