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Satisfaction Not Guaranteed
Information Management services executives aren't delighted by the current generation of IM tools – but the right platform could change their tune
Francesca D'Angelo, Dir, Information Management Commercial Offering
Oct 23, 2020

The data deluge in the life sciences industry has reached a tipping point, making it almost impossible to perform core information management tasks in house. The increase in volume and complexity of data is causing many to adopt IM platforms from third party vendors that promise to drive greater speed, transparency and compliance in IM tasks across their product portfolios.

But our recent survey of 300 IM services executives show they aren’t thrilled with the results so far. Adoption rates continue to lag and this is already an important indicator of how current solutions don’t seem to be attractive “enough” for the demanding audience. Only about 60 percent of companies use product master data management, customer master data management, data warehouse for sales analysis, and artificial intelligence features; and less than half are using reference databases or payer master data management. Among these users only 35-42 percent of respondents are ‘extremely satisfied’ with any of their IM solutions. IM solutions include a variety of components such as master data management, data warehouse, reference database, and artificial intelligence (AI) features.

It is important to note that the lack of satisfaction is due to a number of reasons – not all of which are the vendors’ fault.

Siloed systems

Historically, companies begin adoption of third party IM platforms by deploying individual master data management and data warehouse management solutions within specific business units or regions. In many cases, they work with their vendors to customize these platforms to meet the immediate needs of a single user group rather than thinking more broadly about its application over the long term. This can be a simple way to test the technology and see immediate measurable gains. However, these customization efforts can backfire if the platforms aren’t adaptable to other use cases or to the evolving needs of the team.

When companies select these platforms as one off solutions, there is also a risk to find themselves with a patchwork collection of IM solutions that cannot be integrated. The resulting data siloes limit access to individual data sets, and reduce their ability to get complete insights and conduct meaningful, far-reaching analysis.

These problems can be avoided when companies create an IM roadmap for the business that considers the long term information management needs of the enterprise as part of the technology vetting process.

The technology is still evolving

Business leaders are also somewhat dissatisfied with the evolution of AI in IM systems. Healthcare industry companies are eager to leverage the promise of AI, machine learning, and natural language processing to achieve automation and predictive analytics.

While the current generation of IM platforms is heading in that direction, the technology hasn’t matured to meet business leaders’ vision of what AI and machine learning should be able to do – and users find that frustrating. According to our survey, 91 percent of respondents want to significantly increase the use of AI in their data management activities, and 46 percent said machine learning was the feature they most wanted but didn’t feel was widely available.

Vendors are actively developing these features, but the level of sophistication required to leverage AI and machine learning for healthcare industry IM applications is considerable. These are not plug-and-play solutions. To achieve effective automation and reliable predictive analytics, users need to be able to access large integrated data sets, and have the expertise on their teams to generate well-defined business questions, and to train ML algorithms how to identify relevant trends. Achieving the level of data management sophistication that life sciences companies need to harness these features takes time, money and talent.

Reference database

Only half respondents reported adoption of reference data, and the general level of satisfaction is not extremely high (especially in EBP/small companies), although a big percentage report willingness in increasing the adoption of such solutions.

This can be linked to several factors, including quality, flexibility of the solution and availability, but it’s also fair to assume that the low level of adoption of master data management solutions can be considered an important related factor: without an MDM reference data are typically plugged into each consuming system directly, this generates a lot of point to point connectors and does not really allow leveraging the reference data at enterprise level.

Once again, the need for technologies that allow easy integration and consumption of information comes along.

How to get what you want

IM technologies and offerings are continuously evolving, but the low adoption rate and low levels of satisfaction reflects limited maturity on both sides, and potentially a mismatch between what vendors are offering, and what clients think they need. The only way to ensure that both sides benefit is for clients to talk more strategically with providers about their expectations and how best to address them and for providers to incorporate client feedback in the solution roadmap. Through these collaborations, providers can get a more intimate sense of the buyers’ needs and help them develop a realistic product roadmap that is relevant to their needs.

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