3G, a private equity company famous for its mergers and acquisitions, backed InBev in 2008 to acquire Anheuser-Busch and implemented its zero-based budgeting strategy. Soon, AB InBev started posting profits and reached $40 billion in revenue in 2012. 3G’s strategy is simple: acquire a company, gather all data, and then slash down expenses to boost profit margins.

The past is filled with similar successful mergers and acquisitions, such as Vodafone and Verizon, Google and Android, and AT&T and Time Warner. However, it is also plagued with mergers and acquisitions disasters, namely Quaker Oats and Snapple, America Online and Time Warner, etc.

When mergers go well, they create synergies, cut costs, and create new revenue streams, but it is easier said than done. There are various hurdles involved, such as inaccurate data, overestimated valuations, limited resources, and integration of systems and processes.

Data mismanagement is one of the challenges to creating synergies post mergers and acquisitions. Data storage forms can be completely different and incompatible; two companies might capture and manage data differently, and varying standards, formats, quality, and relevance can prove to be an obstacle.

Data Consolidation appears to be the answer to solve data challenges post mergers and acquisitions. Let’s see what data consolidation is and different strategies organizations can adopt to simplify the process.

Data consolidation is often used interchangeably with data integration. It refers to the process of combining data from multiple sources into one central repository. Data consolidation creates a single source of truth for an entire organization, which improves accessibility and data quality. It allows an organization to see any changes made to data.

An organization’s task does not end at deciding data consolidation as the solution to data challenges. To conduct successful data consolidation, an organization also has to decide on the right strategy after evaluating its resources and data volume.

ETL refers to the process of extracting data from heterogeneous sources, transforming it, cleaning it, and then loading it to the desired destination.

The first step in the ETL process is extracting data. Data is stored in various places such as existing databases, CRMs, ERPs, mobile apps, and on cloud. Research indicates that 18% of the companies use 20 or more data sources for decision-making.

Once data is extracted, it is put through a series of transformations to make it fit for use. Some common transformations include cleaning data to remove inconsistencies and redundancies, join transformation to combine data in a single view, standardization to apply one format to all data, sorting, and verification.

While transforming data, organizations can also apply data quality and validation rules to ensure that data entering the destination meets business criteria.

After transforming data, an organization can upload it to a destination it prefers; it can be a data warehouse, another database, or even a cloud destination.

There are two ways to load data into a destination: incremental loading and full loading. With full loading, all data goes into the new destination at once and incremental loading compares existing data and only loads any new records.

ETL: Build Vs. Buy

Once an organization decides that it is going forward with the ETL strategy, it needs to make another crucial decision about the process whether to buy an ETL tool or build one. This decision can make or break data consolidation projects.

If a company decides to build an ETL process, then it has to make considerable investments in hiring developers; developers are expensive resources and hard to find. Then, it takes a lot of time to build a pipeline, build connectivity with all sources. The process is prone to errors and is time-consuming.

On the other hand, a company can opt to purchase data integration software. These tools already have built-in connectivity, so there is no need to reinvent the wheel.

Data virtualization is another way to get a unified view of all data assets. Data virtualization is different from the ETL approach because there is no need to transfer data from all sources into one destination.

With virtualization, data stays in its source, and a logical virtualization layer makes it possible for end-users to run queries and conduct analysis.

Data virtualization is a modern approach to data integration. With this approach, data stays in its systems, and the virtualization layer fetches data in real-time for reports and analysis.

The strategy a company chooses depends on the goals and resources it has. Data virtualization projects can be done more quickly and at a lesser cost than ETL. This doesn’t mean that a company should not consider ETL because ETL tools have made it easier to carry out data integration projects. Some tools don’t even require coding and have a small learning curve, which means a company doesn’t have to overburden its IT department.

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Source: https://datafloq.com/read/consolidating-data-post-mergers-acquisitions/17792