For many companies looking to grow, mergers and acquisitions are a part of the strategy. In fact, corporate mergers and acquisitions are at a high and a report forecasts continued acceleration of M&A activity. But, are growing companies setting themselves up to make the most of all acquired data assets? When undergoing rapid expansion, it's easy to overlook what has been gained.
A good way to think about it is as a cluttered attic. There could be a valuable antique hidden in a dark corner, but if you don't know how to navigate or organize the attic, you might never find it. Missing out on a beautiful heirloom would be upsetting, but when applied to business, it means missing out on data that could spur your next big breakthrough. For companies undergoing dramatic growth, having a data strategy is critical.
Not sure where to start? Here are three steps to consider for an effective data strategy:
1. Take inventory
An important part of any M&A strategy is to perform a data asset inventory. Think how much easier it would be to navigate the messy attic if you had a list of everything in it. You might not know exactly where the prized antique is located, but with an inventory list, you at least know to look for it. Similarly, conducting an inventory of all the target company's data assets will set you up for success in the early stages of the deal.
The first step is to bring all the relevant players—data scientists, data governance leaders, tech leaders and others—into the process early on. Together, this group can identify and prioritize all assets that are to be acquired. Additionally, time should be spent exchanging data samples to understand if the format is compatible with the parent company's technology and systems. While, this can be a time intensive process, it's well worth the investment and ultimately, can help your business determine the value of the potential acquisition.
What if the deal has already closed without this step? Swift action should be taken to interview key data and technology stakeholders from the acquired company to gain an understanding of what data they have and where it's stored. This human knowledge is especially pertinent in companies with a long history that likely have intangible data that has never been digitized, or perhaps, even written down. Timely action to gain this insight is key considering talent turnover can be high following a merger or acquisition.
2. Harmonize your assets
Just as it would be important to organize the messy attic to make it functional, bringing order to data chaos is necessary to gain business value from it.
A common challenge when companies merge is that their data doesn't seamlessly synchronize. There are often discrepancies in coding and technology that prevent harmony. Creating a common vocabulary that informs structured data models can help make sense of it and ensure all the data works together.
Developing a common data language is not an easy task, especially for organizations that don’t specialize in data management. At CAS, we often help organizations do just that—we strategically index and curate their scientific data so they can efficiently and effectively find mission-critical information.
3. Find the value
Data is only as valuable as the insights you extract from it, making an effective data strategy about—perhaps, most importantly—data analysis. For example, finding the antique in the attic is great, but won't do much for you without an understanding of its history or worth.
Or, consider a store where shoppers use a rewards card. The store collects massive amounts of data from every shopper through the card. But rather than drown in the volume of information, they're able to drive business value by analyzing the data to inform their sales strategy. They use the data to tell them when to put certain items on sale, what types of coupons to offer customers and more. Likewise, any company that wants to drive business success through data must invest in strong data analysis.
One of the most exciting ways CAS supports clients is through our integrated approach to data analysis. Our application of best-in-class human insights and advanced technologies—like machine learning—allows organizations to uncover and unlock insights that would otherwise be overlooked.
Are you ready to get the most out of your data assets? Learn how CAS can help.