The era of Information brought along the need for organizations to collect large silos of raw data. From survey forms to after-sales analysis, data of every size and format are consumed by hungry analysts in pursuit to derive meaningful insights that would put their organization ahead in the market.

Amidst all the hullabaloo, this data-driven market also begs for a crucial question: Are organizations processing all the data at their disposal in arriving at a conclusive solution of the trends? The answer, to many data junkies’ dismay, is a shocking “No”. In reality, the percentage of usable data barely scratches the surface of organizations’ reservoirs; between the fresh input data from a customer to a processed pattern, as much as 90% of the data went unstructured and therefore unused as per IDC.

This gap between expectation and reality puts decision-makers, especially in Sales, amidst a large portion of unorganized, unclean and unusable data. Sales is one of the most important parts of the business that drives and thrives on data. Sales analytics, therefore, determines how organizations convert their data into marketable-solutions.

Dark Data Matters

According to Gartner, the term “dark data” is defined as the information assets that organizations collect, process and store, but generally fail to use for analytics, business relationships or monetizing purposes. The risk in growing dark data within organizations is that their Sales prediction with just a portion of processed information may prove to be unsustainable for today’s fast-paced market trends. It is imperative that, to create a definitive long-term vision for their business, decision-makers cannot limit themselves to patterns derived from just 10% from the sea of information available at their disposal.

Data Discovery to the rescue

Data discovery is an agile solution that allows individual decision-makers (with or without expertise in data analytics) to add, process and create trends from raw data on the go. The tools that are currently available in the market, with its varied levels of complexity, inputs data of any format and allows the user to process them the way they want in the range they desire. Data discovery tools has allowed businesses to remove their dependency of experts and complex processes, explore the depths of their dark data warehouses and arrive at choicest patterns that could immediately be converted to monetizable decisions.

Harnessing Sales Analytics

With the rise and rise of Data Discovery, the markets are now flooding with tools that could be deconstructed to build a personalized analytics engine for the organization. Sales Analytics, being one of the largest contributors to decision-making, plays a significant role in shaping an efficient data discovery tool.  Creating any tool that inputs sales analytics is expected to perform two major operations: Predictive and Prescriptive analytics.

Predictive Sales Analytics define the scope of sales that envision a plan, a road map for organization’s future. Crunching numbers and creating intelligent predictions are the basis of this research and there can be no compromise on the quality or efficiency of the data. Tuning a data discovery tool to perform functions ranging from simple sales projection operation, opportunity discovery to complex forecasting metrics that could set the immediate and long-term goals for the organization is imperative.

Prescriptive Sales Analytics creates the path to harvest the predictive data that has been accumulated to shape businesses. Data discovery tools aimed to address prescriptive analytics must be moulded to absorb, assimilate and analyse the trends of the future with the capabilities of the present to create a sustainable plan to achieve the targets.

The growing popularity of data discovery tools amongst enterprises can be attributed to the fact that these tools empower businesses as well as individual users by allowing them to self-service their data. The trend is likely to grow stronger, as Gartner predicts that by 2017 most Business users and Sales personnel will have access to such tools to prepare and process their data. The prediction also states that by 2017, the market would have been upgraded with smart data discovery capabilities that explores even more complex dimensions of big data.

Traditional Business Intelligence (BI) solutions requires experts and analysts to build intelligent programs to cleave through the piles of bytes to derive a meaningful pattern. The process is expensive, time-consuming and highly structured for the likes of the exponentially growing competition. Data discovery has successfully bridged this gap between organization’s need for usable data and the process rigidity of the traditional BI methods. It is now up to the decision makers to define the right solution in their quest to tame the data beast.