Data is the glue that binds together people, processes and technology. In the modern business, data provides the most comprehensive account on what occurs in the business as, generally speaking, data flows through all corners of the business. From the creation or acquisition of data, to its curation and management, to its end use as the ingredients of analysis or as a stand-alone product, data courses through the organization, adding value, risk and expense along the way.
When viewed from the right perspective, data indicates how well an organization is optimized.
Many companies attempt to strengthen their business and their bottom line through Mergers and Acquisitions, yet many of these ventures fail. One of the primary reasons for failure is the underestimation of the costs, in time, money and ‘pain’ associated with integration. Sometimes the data from the two organizations just doesn’t fit together. Wouldn’t it make sense to do a comprehensive profile and analysis to compare all of the data before proceeding with a merger or acquisition? With the right algorithm, you can detect trouble spots in the data before the firms are brought together.
Our view is that data holds the competitive advantage of most any firm.
The data that an organization collects and deploys, can in itself, be their competitive advantage. Proprietary data is becoming a huge part of many firms’ business plans. Even when data is not at the core of the business plan, when viewed from the proper perspective, data reflects the competitive advantage of the firm.
The question then becomes, how can you accurately assess this in the tight M&A timeframe? You need a streamlined process that leverages technology to do a fast, yet robust analysis.
In brief, our solution involves three stages:
1. Stage the Data
2. Execute Analysis
3. Act on Insights
The first stage involves collecting data from both firms and placing it in a secure ‘clean room’ to prep for analysis.
The second stage is where the heavy lifting occurs. With a cutting-edge algorithm that utilizes machine learning and topological math, you can make a quantitative comparison between the two sets of data. With advanced visualization techniques, you can find hotspots where the data does not match up. You must then determine how big of an impact the mismatched data will have on proposed synergies.
The third stage is where the execution of the analysis occurs. If the two firms’ data does not match up, the buyer might adjust their bid. If they find the data to be a good match, the acquired firm might demand a pre-determined bonus.
This is a snapshot of our proposed innovation to the M&A process. There is no reason for a firm to acquire or merge with another firm without having a clear picture of the risks and potential incompatibilities that can be gleaned from the data.
To see the full version with detailed steps, please see the full post on LinkedIn. To learn more about how the Data Clairvoyance Group can ignite business oriented data management in your organization, contact us.