Our Process

Today "Big Data," analytics, artificial intelligence and the "Internet of things" attract a lot of industry's attention.  But, using Big Data to its full potential is more about management and effective end-use than it is about technology.  Data scientists can provide a series of smart data presentations that yield perceived insight.  But true insight - real business trends, drivers and detractors - can be hidden if the organization fails to develop a deeper understanding of data's implications and cross functional relationships.

A subjective AND quantitative analysis of source data, DatabaseDNA, a proprietary, deterministic measurement and validation process, is the primary step in the informationalization process.

• With an objective, measured and prioritized display of raw data, consisting of conflicting and supportive "driver" elements.
• Management and end-users are presented with a visual, data array that can easily identify trends and anomalies that may necessitate re-organization of data and improved cataloguing such as different and/or new segmentation - market, product, customer or sales size and hierarchy and geography - with aggregation and disaggregation of the data.
• From there it is a gaming, "what if" methodology to determine what's important to know with dynamic discovery using immediate feedback from the enterprise' subject matter experts.

In two real-world examples, standard reporting displayed revenue cost with simple sorted ranking order.

Analysis of the relationship of cost and commission did not reveal an unusual trend between cost , revenue or profit.

The process of informationalization revealed hidden customer life cycle, churn and organic sales growth issues (as the sample stop-light display shows)

• Among the top 50% of sales there was a 30% decline in organic revenues - additional analysis showed that either customer churn reported as new revenue or transferred sales territories indicating individual sales division expansion were actual losses to the enterprise.
• Further analysis indicated a greater loss of market share than reported.
• While profit trend remained positive, analyses of forecasts indicated sales and profit trends were not sustainable.

• An aggressive re-structuring of the database:
• Create a smart customer identification system to track customer life cycle, customer hierarchies (customer, child, parent) and tie to the proper sales divisions.
• To mitigate business disruption, expand inside sales and lead generation.
• Use cost savings, primarily from commissions.