3 Business Challenges That Rely on Successful Data Migration
By Lars Thielke | October 25, 2018
Amid rising research and development costs, downward pricing pressure, and geopolitical events impacting taxation, Life Sciences organizations are facing increased pressure from their shareholders to maintain growth. In order to forge ahead, companies are on the constant lookout for growth and revenue-boosting business opportunities, several of which, in turn, trigger the need for a data migration. Mergers and acquisitions, asset swaps and full-scale system upgrades are three such opportunities, but in order to effectively capitalize on the investments of effort and time that they require, corporations need to be digitally ready for the challenges of data migration.
Read on to learn more about 3 common business challenges that expose companies in the Life Sciences industry to potential data migration risks.
Mergers and acquisitions
Life Sciences organizations need to position themselves favorably not only to their shareholders, but also to like-minded corporations with whom they may one day need to merge. However, it’s estimated that about 40% of a company’s data is made up of Redundant, Outdated or Trivial information (ROT), making many companies unattractive to acquire and ill-prepared to be an acquirer.
Acquiring niche companies is one of the fastest ways to grow in the Life Sciences industry, but organizations that are not prepared to digitally merge, will miss out on what this opportunity has to offer. For organizations in a position of acquiring another, this means looking for ways to mitigate the risks of data migration when aggregating the combined data assets and sorting through significant ROT from any company they purchase. The ideal outcome being clean, classified information, enabling the acquiring company to find information quickly, easily and reliably.
A further data migration challenge remains: if an enterprise merges with another that has compromised data-management practices, they could be exposed to potential fines and punishment over a breach in regulations. In the highly regulated Life Sciences industry, this could lead to an inability to recover the cost of the merger, itself, on top of any new compliance-related penalties.
The level of “data trust” an organization holds is directly related to how well they manage their data. Life Sciences enterprises that are looking to be acquired, should clean and classify their data and apply file analytics as a means of preparing for the upcoming, inevitable data migration. Doing so will improve the searchability of their data while simultaneously raising their company’s level of data trust, thus making them more attractive and valuable on the open market.
Product and asset swaps
To optimize their portfolios in focus segments, Life Sciences organizations are participating more frequently in product swaps. A notable example took place in 2016 where Boehringer Ingelheim, a global pharmaceutical company, agreed to exchange its consumer medicines division, valued at €6.7 billion, for health-care counterpart, Sanofi’s, animal health business, valued at €11.4 billion. The deal enabled Boehringer to prospectively become one of the largest global players in the animal health segment. In turn, it also positioned Sanofi as a leader in consumer healthcare.
Whether a transaction involves changing or exchanging whole assets, business areas or individual products, this type of event results in both parties acquiring volumes of unstructured content in each other’s file shares. Instead of simply lifting and shifting information from one enterprise to another, product swaps present an opportunity to move, improve and elevate the data.
One of the key challenges of data migration exposes itself when an enterprise acquires an entirely new and unknown repository of information. Being able to smoothly and completely incorporate new file shares into your existing data repertoire will drive growth and mitigate any data-related disruptions.
In these times of rapidly changing technologies, Life Sciences companies need to work hard to keep pace with their competitors. Not only is this necessary to position them as industry leaders, it also puts them in a more positive light among shareholders and investors.
In Forrester's Global Business Technographics Priorities & Journey Survey, 2017, 70% of organizations identified their corporate priority as improving IT systems to better support new digital technologies. In order for them to advance in a digital era, and do away with legacy systems, massive upgrades are essential.
However, it’s important to distinguish between the data and the system that houses it before an upgrade turns into a full-blown data migration disaster. An upgraded system has the power to reduce efforts and increase productivity, when it’s fed with clean, classified content. But much like a bad diet, if a company fuels its system with dirty data, it will be limited in its results. Not to mention that it was likely the ROT and unstructured data, rather than the outdated system, that needed an upgrade in the first place.
The lazy, but unfortunately common, “lift and shift” approach to data migration, can result in a significant waste of time and resources, in the long run—without any concrete business value to show for it, even in the short run. Competitive companies planning an upgrade need to think beyond the simple process of moving their data between repositories. Instead, they need to find ways of adding value to their content before, during, and after the data migration. This will result in enriched information that’s easier to find, easier to use and better positioned to drive actionable business intelligence. In the case of legacy systems and repositories, these could be decommissioned and the data consolidated to create one enterprise-wide “point of truth” for data.
The main challenge of data migrations is that they need to be done with forethought in order to generate positive ROI when facilitating any mergers, acquisitions, product swaps, system upgrades or consolidations of legacy systems. When acquiring any new repository of information, data migration is a critical element in ensuring a smooth and seamless transition. Clean, classified content is the key to preventing and mitigating data migration risks for Life Sciences organizations looking to grow.
To learn about Adlib’s data migration approach, and to find out how you can do more than just “lift and shift” your data, check out our Enhanced Data Migration page.
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