Very often, in spite of investing in technology, trained staff, and expert consultations, businesses lack a clear understanding of their daily operations. While business managers struggle to maintain their organization’s competitiveness, very powerful solutions are often hiding in customer, supplier, or market data that need to be extracted from emails, Word or PowerPoint files, social media, webcasts, or graphics and analyzed for actionable insights. Due to the recent in data sources, the current crop of BI systems must be strengthened to handle unstructured and semi-structured data to make the analytics useful. A very significant part of the modern data transformation process is the data storage, integration, and preparation phases. As this Mu Sigma article on analytics ecosystem points out, while the analytics capabilities of BI tools can deliver results, these results have to be transformed into highly visible insights for business executives, manager, or employees to instantly decipher the answers they are looking for to arrive at future decisions. While descriptive, inquisitive, or prescriptive analytics can successfully describes what happened, why it happened, and what is likely to happen in future—none of these analyses can really be of any worth to the end user unless the system also tells how these explorations can “prescribe” solutions to mitigate the adverse effects of the current or future events. Incidentally, all the above four types of analyzes are required in conjunction to “enable the creation of business insights.” Business data at each of the exploration levels still remains data; for data to convert into insights, an in-depth analysis of the visible data patterns or trends and the invisible relationships among them needs to be conducted by the BI system. This means, that a powerful BI system will be able to take the observations from all the preceding analytics and then combine the results to conduct a holistic study of interrelationships between different sets of results. Data to Insights: Blueprint for Your Business indicates most businesses use data as a decision-support tools rather than deriving future actions from them. However, for BI tools to be really effective as a business aid, organizations must utilize their systems to transform the analytics into actionable insights. Hence, while asking questions about the business data, data analysts must keep in mind that the context, value, and outcome of the data are as important as the data if not more. Many good BI systems provide a clear framework for turning the raw data into meaningful stories. Many large organizations sitting on piles of data fail to make use of the data as their existing BI infrastructure does not deliver clear insights that drive your business. From Data to Insights: The Journey First.com au blog offers an a la carte menu for realizing actionable insights from business data. The process involves filtering, sorting, grouping, and presenting the data through the underlying steps to deliver visible insights: The measurable KPIs ought to be aligned with overall business goals. Customer actions must be monitored daily as any change can indicate success or failure of business operations. Conversions must be carefully monitored vis a vis applicable marketing campaigns Use tools to study patterns or relationships between analytics results. Use tools to convert quantitative data into qualitative evaluations. Insights must indicate corrective actions. Data Was Always Available Although heaps of complex business data have always been available, businesses have not been able to harness the underlying power of that data through due to inadequate data aggregation and visualization tools for enhanced visibility. It is not enough for BI systems to perform sophisticated analytics on the available data; these systems must also be able to present the data to end users at varied organizational levels in the most user-friendly manner. Regardless of tech savvy or technical knowledge of BI analytics, the end users at any organization ought to be able to derive useful knowledge from the presented business insights through descriptive and predictive models, and statistical analysis, to derive business insights to make quick and effective decisions.
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