IBM’s Big Data and Analytics portfolio needs more tailoring for life sciences

Andrew Brosnan, Senior Analyst, Health Sciences

At its recent Big Data and Analytics Analyst Insights Summit in Toronto, IBM laid out its portfolio of technologies and services specifically for business analytics. The portfolio is a robust and comprehensive offering, encompassing all types of analytics, with specific tools and technologies across the “stack” for organizations of all sizes.

The phrase “easy, fast, and smart” was a recurring theme throughout the event, highlighting IBM’s commitment to providing a broad array of analytical tools and technologies that are easy to use and fast.

For many analytics vendors in a Big Data age this has been a rally cry, but IBM is well positioned to provide such capabilities to life science organizations because of the breadth of its expertise and the depth of its research and development (R&D).

Life science companies are faced with increasing volumes of fast-moving and diverse data (Big Data). They can no longer afford to be introspective; to drive business value they need to merge internal and external data, and structured and unstructured data.

IBM’s technologies and solutions support the spread of Big Data analytics throughout the organization through ease of use and performance, and can be leveraged in life science organizations, but more can be done to develop analytics solutions that address the unique challenges faced by the life sciences industry.

Analytics for pharma will cure its ailments

The need for business analytics within the life sciences sector is great. The industry is facing tremendous challenges, and undergoing change as a result. Biopharma companies are under increasing pressure to deliver greater value and efficacy in medicines, while price cuts are squeezing operating revenues.

Furthermore, large internal R&D departments of Big Pharma firms have not delivered many new and innovative therapies, driving greater outsourcing of R&D and partnerships within both private and public organizations. Changes to the operating model are not limited to R&D; biopharma companies are also looking optimize their activities across the drug lifecycle to realize greater productivity and efficiency.

As result of technological advances and the proliferation of Internet technologies, large amounts of data are now being created every day – data that helps life science organizations address many of these challenges. But although this avalanche of data creates opportunities, it also poses new challenges for life science organizations: they must not only manage the volume of data, but also make sense of it, and then act on these insights.

However, most users within life science companies lack the specific analytics and technical skills necessary to administer and analyze these large data sets.

Big product portfolio presented by Big Blue

At the event, IBM presented its portfolio of products designed to manage and analyze large sets of diverse types of data to support decision-making and, ultimately, deliver greater value. These included new additions to business intelligence (BI) tools from the Cognos product family and to advanced analytics and decision-management tools from the SPSS product family.

It also presented wider commercialization of Watson’s technology: its cognitive analytics capability can be used within certain industries such as healthcare and for specific activities such as client engagement.

Additionally, IBM demonstrated its purpose-built infrastructure and related software designed specifically to optimize analytic queries of large and diverse data sets that are comprised of both traditional and novel data types. It also announced signature solutions that provide end-to-end analytics solutions comprised of different tools and technologies from with the portfolio.

Its signature solutions are designed to address specific business needs, such as predicting trends and opportunities, building customer loyalty, and optimizing operations. These are to be implemented by IBM’s professional services, Global Business Services (GBS), where it has specific domain experience.

Not to be outdone by Oracle’s engineered systems and SAP’s HANA, IBM has invested significantly in developing performance-enhancing infrastructure technology specifically for analytic queries and workloads. Client testimonials at the event reported significant performance improvement in running analytic queries on large data sets.

These data management and system optimization tools allow for the high-performance processing of large data sets through such technologies as parallel processing, in-memory and in-databases capabilities, columnar databases, and dynamic OLAP cubes.

Importantly, these technologies enable new and robust types of analyses to be performed by enabling exploratory analysis such as data discovery on the entirety of the data rather than subsets. They will be transformative for life science organizations because they will enable simultaneous analysis of diverse data sets, which was previously not possible because the data sets were too large.

More industry-specific tools or extensions required to address unique challenges

These are all positive developments that will resonate within the life sciences industry because it urgently needs such technologies and capabilities. However, specific solutions geared toward addressing the unique challenges faced by life sciences organizations in their business and scientific activities are lacking, particularly in R&D.

IBM does provide some industry-specific Cognos templates for budgeting and forecasting of clinical trials, and its Strategic Intellectual Property Insights Platform (SIIP) provides support for chemical structure content searches. But greater out-of-the-box functionality of BI and advanced analytics for supporting decision-making in the R&D stages of the drug lifecycle, for example, would help life science companies address key challenges such as the cost and time to market of new medical entities (NME).

Furthermore, industry-specific extensions for IBM’s content and advanced pattern recognition technologies could augment its SIIP solution to help biopharma companies uncover new insights in the genetic determinants of disease, and opportunities to expand the market for existing or uncommercialized chemical compounds.

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