Cognitive M&A – Leveraging Cognitive Search & Analytics for Successful Mergers and Acquisitions

Mergers and acquisitions provide one avenue for organizations to grow via synergistic gains, strategic positioning and diversification. Even with an abundance of M&A activity, mergers tend to fail at the business process and information integration levels. The success of a merger can be greatly enhanced when business processes are integrated and information is seamlessly unified by gathering it from both organizations, analyzing it, establishing clusters of semantically similar information, and finding common patterns. Cognitive search & analytics platforms provide the necessary capabilities to accomplish all of this, thereby helping facilitate merger and acquisition initiatives and significantly increasing the odds of success.

ANATOMY OF A SUCCESSFUL MERGER

Let’s envision the details of how this impacts the relevant stakeholders. At the outset, a cognitive search & analytics platform provides the organization with unified access to information from both organizations and beyond. Users can leverage out of the box machine learning algorithms to explore and navigate this information. For example, the Clustering algorithm groups documents into topically-related clusters by analyzing the content and metadata. This is very useful for topical navigation and helps stakeholders identify similar documents based on named entities within the content. Automated classification is another useful technique for unifying information and improving navigability. In certain circumstances such as when classification rules do not exist but a properly classified sample set of content does, a Classification by Example algorithm can automatically create a model from the sample set, which can subsequently be applied across the combined set of content from both organizations to further enhance findability for stakeholders.

Sinequa CollaborationMachine learning algorithms can also help match experts with other experts as well as relevant documents across the consolidating organizations. This is done dynamically by analyzing what people write and collaborate around to compute user profiles, which are subsequently analyzed to compute “peer groups” that connect stakeholders with similar interests and expertise across the consolidating enterprise. With these peer groups established, experts can be more effectively presented with relevant content using a collaborative filtering technique that compares preferred content across the peer group and surfaces valuable content to members of the peer group who have not previously been exposed to it. As you can see, a cognitive search & analytics solution facilitates smart information sharing across the consolidating enterprise. Usually a lack of sophisticated security controls impedes greater openness between consolidating entities. A search-based application, however, respects existing security profiles—making it easier to merge infrastructures securely.

A cognitive search & analytics solution also helps to identify areas of risk and to solve outstanding issues before financial consequences occur. For example, risks could include content containing Personally Identifiable Information (PII) or content with no security associated. This is done by employing text-mining agents (TMAs), which provide out-of-the-box rules-based capabilities to extract elements from unstructured text. TMAs can be configured to incorporate terms and phrases specific to any part of the business. A cognitive search & analytics solution enables a quick, seamless and successful consolidation of organizations. Typically, in a large enterprise this is done as a series of search-based applications (SBAs) that each pull from a Logical Data Warehouse (LDW), which is essentially central cache of unified information.  In the next sections, we will look at specific areas of the business that typically benefit the most from this approach.

SALES AND MARKETING 

Once consolidation is underway, the organization must move quickly to combine sales and marketing activities, sales methodologies, pipelines and channels to drive revenue in the field and promote up-selling and cross-selling into new and existing market segments. The organization wants to minimize any potential lapse in the sales cycle for the newly merged company.

A cognitive search & analytics solution immediately equips sales teams with a single global access point to relevant, real-time and insightful information on products and customers—sales and customer notes, sales processes, product information and sales training are all immediately accessible. As previously mentioned, this is typically done using a dedicated search-based application (SBA).

An SBA for Sales and Marketing could provide unified access to the separate CRM systems and allow for the addition of shared content. As a result, no sales note, customer quirk or prospect is lost during consolidation. An SBA also offers the ability to push alerts and notifications out to users.  As sales representatives learn about new products,

The SBA provides unified access to new marketing materials, sales process documentation, research documents and news articles to assist in training and ensures that they are effectively representing the newly expanded product line. Often when a merger occurs, customers know more about the products from the other company than the sales reps. With a cognitive search & analytics solution in place, an empowered and unified sales team can competently sell the acquired products and services.

FINANCE, ACCOUNTING, AND HUMAN RESOURCES

Finance, accounting and human resources are other key departments that need to be unified. A dedicated SBA provides a complete and consolidated access to information from the disparate ERP systems.

A cognitive search & analytics solution provides administrators and content curators with the visibility across the enterprise necessary to manage all documentation from both parties related to the merger effectively and securely.

Multiple IT systems and finance systems always increase the complexity of a merger.  Organizations acquiring a large IT infrastructure need to identify the systems acquired and the value of the data in these systems.

A cognitive search & analytics solution enables the required visibility and helps to expose any redundant or unused systems that might be eliminated. For example, a cognitive search & analytics solution can be used to monitor and analyze usage of underlying repositories and applications to show what sources are being used less frequently. Some organizations have even been able to standardize and eliminate the need for multiple search applications. In other cases, a cognitive search & analytics solution works as a stop gap providing access to legacy systems that should be migrated, thereby reducing the cost of forcing an immediate migration.

Organizations can leverage Sinequa’s scalable platform as the cognitive search & analytics solution to connect information across the consolidating enterprise by leveraging previously mentioned machine learning algorithms like Clustering, Classification by Example, etc. as well as more traditional rules-based enrichment techniques. This helps the organization deliver unified access to stakeholders, ensure accuracy, reduce risk and gain insight into the complex systems across all affected departments. Enterprise Application Integration is usually a multi-year project. While cognitive search & analytics solutions do not replace such an integration, they provide comprehensive visibility in a very short time.

RESEARCH AND DEVELOPMENT

The cost of R&D increases when multiple teams unknowingly work on solutions to the same problems or fail to recognize and utilize the work done in past research. Cognitive search & analytics solutions positively impact R&D by accurately combining research data from the consolidating organizations, giving users real-time access and reducing the duplication of content, efforts, and sometimes entire projects. They do this by connecting experts working in the same subject area.

Sometimes a key driver in a merger or acquisition is gaining access to intellectual property, which often includes the expertise of the other organization’s knowledge workers. Sinequa’s Find the Expert capability gives employees from each organization the ability to discover the most knowledgeable people on a variety of topics, to view their profiles and to find associated information. This accelerates R&D discovery by enabling users to navigate information in clusters—leveraging the machine learning algorithm—and by content refinements.

These tools enable users to find past research and hidden relationships – including relationships with external experts with whom both companies have collaborated in the past – that would have otherwise been missed, thereby increasing speed-to-market.

The organization is also able to gain greater market share by leveraging and optimizing the information acquired instead of simply discarding projects in progress. One of the key drivers in a merger is the ability to retain and share as much knowledge as possible. Sinequa’s collaborative features spawn greater innovation in the newly expanded R&D department in the form of capabilities such as tagging, bookmarking and automatic feedback loops. For example, a scientist can comment on an old publication and explain how it relates to new research, which subsequently increases go-to-market speed by enabling broader collaboration.

HOW IT WORKS

Sinequa has developed an innovative and simple-to-use Cognitive Search and Analytics platform that offers Unified Information Access (i.e. information from any source, in any format, whether structured or unstructured, internal or external, through a single platform and user interface) to respond to even the most difficult information access challenges of large companies and organizations. The solution is composed of three main components:

  • A powerful (natural language) analytics platform that gives structure to the most unstructured content. The analysis and indexing of data solve typical enterprise challenges involving multiple sources, unstructured and structured content, multiple languages, high data volume and high update frequencies.
  • Simple and intuitive user interface that brings simplicity to complex search and analytics. Sinequa’s out-of-the-box user interface leverages the familiarity of common search tools enhanced with faceted search tools (i.e. filters) and analytics, giving the user an immediate picture of the information available from all enterprise sources. The UI is easily customizable to reflect specific appearance and navigation goals as well as corporate standards/branding, and is extensible, allowing third-party or custom UIs to be easily integrated via a provided search API (Application Programming Interface).
  • A powerful, open and scalable technical architecture (GRID). Sinequa’s highly-scalable architecture was designed from the ground up to support multiple search needs starting on a single server cluster, providing a cost-effective solution that allows companies to respond to multiple business needs now and in the future with minimal hardware investment (whether the application is deployed on premise or in a private cloud). The architecture is distributed and modular to support virtually any data source addition and growth, often without any additional infrastructure investment.

Leveraging these combined components from within the Sinequa platform has proven to accelerate the integration of business processes and to amplify the expertise of the whole by seamlessly and securely unifying disparate information from each organization involved in a merger or acquisition.

CONCLUSION

A cognitive search & analytics platform facilitates information transparency and communication across the entire enterprise, minimizing disruptions while integrating teams and departments during mergers and acquisitions. Applying this technology as a solution effectively operationalizes the information access, speed and control needed to ensure long-term success in the newly formed organization.

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Join Sinequa at Bio-IT World Conference & Expo 2016 (Booth #421)

Sinequa will present and exhibit at Bio IT World Conference & Expo that will take place on April 5-7 at the Seaport World Trade Center in Boston, USA.

Sinequa For Life Sciences

We invite you to stop by the Sinequa booth #421 to discuss innovative use cases of our solution for the Pharma industry – Sinequa For Life Sciences - and see how our customers raised their competitiveness by implementing our Big Data Search and Analytics solution across the most diverse data silos.

  

Also, make sure to book your agenda and attend our presentation in the Bioinformatics Track #5:

Wednesday, April 6, at 2:55-3:10 PM

“Increasing the Competitiveness of Pharma Companies:
Real Time Search and Analytics Across Structured & Unstructured Data”

Speaker: Xavier Pornain, Vice President of WW Sales & Alliances

Book your agenda

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Finding the Information ‘Needle in a Haystack’

Below is a contributed article from our VP Marketing, Laurent Fanichet (@fanichet). The original version is available on Biosciencetechnology.com.

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Sinequa - Finding the Information ‘Needle in a Haystack’

Digging through volumes of pharmaceutical data in any form, be that of lab reports, experimental results, clinical trial reports, scientific publications, patent filings, to even emails is a gargantuan task.  The data may deal with diseases, genes, drugs, active agents and mechanisms of action and can be textual, structured data like molecule structures, formulae, SAS data sets from clinical trials, curves, diagrams, and more.  When put together, all of this information can be retained in hundreds of millions of documents and billions of database records.

Compounding this volume of information are the billions of database records from internal and external trade sources that may be related to a life sciences project.  World-renowned pharmaceutical and chemical companies, such as AstraZeneca and Biogen, rely on search and analytic technology to solve real world problems by providing a single point of access to information extracted from all these data sources.  Search and Analytics solutions specialize in finding that data ‘needle in a haystack.’

To find that ‘needle,’ advanced organizations turn to the power of Search Based Applications (SBA).  Imagine that your company has an idea, but the data required to obtain meaningful results is spread across multiple business units or even enterprises in different formats.  What if you could quickly develop an application that could bring all the data together and allow you to create search queries to find the data that you require? And more importantly, what if the application could be built in a month’s time using highly advanced natural language processing that allows you to make sense of the complex information in scientific publications or clinical trial reports using artificial intelligence and machine learning from Spark; statistical analysis of structured data; and above all, combined statistical and linguistic/semantic analysis?

Advanced search and analytics platforms index all the structured and unstructured data sources and create a semantically enriched index, optimized for performance in dealing with user search queries.  In fact, some search and analytics solutions even offer as many as 140 smart connectors, ‘out of the box,’ that can seamlessly connect multiple sources of data.These companies integrate your company’s and industry specific dictionaries and ontologies allowing the information to be integrated and indexed, putting your specific knowledge ‘under the hood’ of one platform – making it an intelligent partner for anyone searching for relevant information for his/her subject.

The Pharma industry is starting to efficiently leverage SBAs in multiple ways. A major benefit of SBAs is that it allows companies to find subject experts. A company can quickly get a dynamically calculated list of people with their respective domains of expertise related to your question/subject. The results correspond to an ‘Expert Graph’ calculated from the ‘footprint’ experts leave in texts and data.

To make the most of your volumes of data, look for search and analytics solutions that will also allow you to build on your network of experts outside of your own internal resources.  For example, you can extend your search for experts on a particular subject by ploughing through massive amounts of data, in particular scientific publications, publicly available trial reports, patent filings, and reports from previous collaboration projects, in order to identify the best available experts – “Key Opinion Leaders” – and the organizations they belong to.

It’s also valuable to use a search and analytics solution to access the latest scientific information in your field with automatic alerts.  This is extremely valuable because it allows you to discover research trends in your field and potentially monitor the competition.  Such SBAs may easily cover as many as 110 million documents: all accessible external data sources including publications, Embase, Medline, Scopus, clinical trial reports and your company’s internal data sources via SharePoint, Documentum, etc.

Clinical trials going over many years generate millions of SAS datasets and billions of rows per drugs and studies.  Over time, Biostatisticians face tremendous challenges performing their analysis with the right datasets.  It is difficult to get a comprehensive list of patients having certain diseases within trials on a drug; ensuring completeness of results is nearly impossible with traditional tools and processes.

With powerful search and analytics indexing technology, scientists are able to search complex content with very precise criteria.  They can retrieve subjects that have shown certain diseases by specifying exact or fuzzy values on the AELLT variable for instance.  The scientists can then filter based on additional criteria like age and can combine about 900 CDISC variables and add any specific variables you may have.  They can search datasets based on the structure and metadata. Possibly even more important, the scientists can even search across many drugs and studies, merging current data silos.  In total, pharmaceutical company scientists are transforming their growing clinical trials data to a valuable asset that can be searched in real time.

Taken all together, an advanced search and analytics platform is able to leverage data indexing and analytics technology and enable organizations to create their own Search Based Applications.  In doing so, companies are able to:

  1. Accelerate research and time-to-market of drugs
  2. Quickly find experts on a particular subject
  3. Find key opinion leaders and R&D cooperation partners
  4. Push latest news on a subject to partners
  5. Monitor publications on particular subjects

The result? That ‘Needle in the Haystack’ just became much easier to find!

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