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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What is Cognitive Search? How a New Generation of Platform is Transforming Enterprise Insights?

Despite the effort from technology vendors to deliver relevant, contextual, and actionable insights with their applications, most organizations have been slow if not reluctant to embrace these advances in search-driven experiences. In fact, a lot of companies have been burned by their past enterprise search experiences.

The good news is that something is shaking the world of Enterprise Search – some would say ‘finally.’ New industry investments and R&D effort are changing the search experience to provide more relevant results and deeper insights to users in their work context.

As we enter the era of “cognitive computing,” new search solutions combine powerful indexing technology with advanced Natural Language Processing (NLP) capabilities and Machine Learning algorithms in order to build an increasingly deep corpus of knowledge from which to feed relevant information and 360° views to users in real-time. This is what leading analyst firms call “Cognitive Search” or “Insight Engines.”These cognitively-enabled platforms interact with users in a more natural fashion, learn/progress as they gain more experience with data and user behavior, and proactively establish links between related data from various sources, both internal and external.

In a recent brief, Forrester defines cognitive search as:

“Indexing, natural language processing, and machine-learning technologies combined to create an increasingly relevant corpus of knowledge from all sources of unstructured and structured data that use naturalistic or concealed query interfaces to deliver knowledge to people via text, speech, visualizations, and/or sensory feedback.”

How does cognitive search work to deliver relevant knowledge?

  • It extracts valuable information from large volumes of complex and diverse data sources. It is crucial to tap into all available enterprise data whether internal or external, both structured and unstructured, to provide deeper insights to users in order for them to make better business decisions. Cognitive search provides this connection to provide comprehensive insights.
  • It provides contextually and relevant information. Finding relevant knowledge across all available enterprise data requires cognitive systems using Natural Language Processing (NLP) capable of “understanding” what unstructured data from texts (documents, emails, social media blogs, engineering reports, market research…), and rich-media content (videos, call center recordings..), is about. Machine Learning algorithms help refine the insight gained from data. Trade and company dictionaries and ontologies help with synonyms and with relationships between different terms and concepts. That means a lot of intelligence and horse power “under the hood” of a system providing “relevant knowledge” or insight.
  • It leverages Machine Learning Capabilities to continuously improve the results relevancy. Machine Learning algorithms (amongst the most popular ones: Collaborative Filtering and Recommendations, Classification by Example, Clusterization, Similarity calculations for unstructured contents, and Predictive Analysis) provide added value by continuously refining and enhancing the search results in an effort to provide the best relevancy to users.

Thanks to new technology advancements, cognitive search brings to data-driven organizations a new generation of search enabling them to go far beyond the traditional search box, empowering its users to get immediate and relevant knowledge at the right time on the right device.

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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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Digital Workplace: Digitized Chaos or Information at your Fingertips?

Digital Workplace

You have a digital Workplace, of course. Does it fulfil all the expectations you had when you went “all digital”? Or is getting at the right information still too complex, too cumbersome and time-consuming? Companies often need specialists to extract information for each specific work context. That is not agileand it’s in total contradiction with the modern digital workplace principles promising “information self-services”.

In decent Digital Workplaces, you find information, not data! And this information must be comprehensive and relevant, and delivered instantly, since in the era of digital business models, there is no time to sift through tons of data when you need information. At best, information is delivered proactively, in order to gain time, increase productivity and improve decision making.

Now, many of you may be wondering: “how to create value from data in increasingly digitalized businesses?”; “how to extract relevant information from big and diverse data and then, deliver precise and relevant information to each and every person at the right time?” This might seem like an elusive goal as we create more data than ever in digitalized workplaces, potentially increasing chaos every day.

To overcome these challenges, we need to simplify the digital workplace for users. This requires high performance systems of data retrieval, analytics and information delivery.

In the past, organizations have installed data warehouses and search engines to help people find relevant data. Many of these never delivered on the expectations – and the needs – of users and organizations. They were lacking in analytical power and in performance when faced with large and growing amounts of heterogeneous data and with the need to combine analysis of structured and unstructured data, including most prominently natural language processing (NLP) for a while range of languages.

The new generation of enterprise search platforms have evolved into whatGartner calls “Insight Engines”.

According to this leading analyst firm, 25% of large organizations will have an explicit strategy to make their corporate computing environment similar to a consumer computing experience by 2018; 46% have a digital workplace initiative underway and 4% have appointed a Digital Workplace leader.

As usual, the bright new digital future cannot be “bought” with a new piece of technology. It requires a change of mind-set and a change in corporate culture.  Nevertheless, be aware that the digital workplace technology you select can either facilitate or impede adoption and change of culture.

Gartner specifies these Digital Workplace Principles : Contribution/ Enthusiasm; Digital Dexterity; Autonomy

#1 Contribution/ Enthusiasm: By promoting employee engagement, digital workplaces create a workforce that makes discretionary contributions to business effectiveness

#2 Digital Dexterity: Creating a “consumer-like computing experience” to enable teams to be more effective

#3 Autonomy:  Exploiting emerging smart technologies and people-centric design to support dynamic non-routine work

To step into the era of the reimagined Digital workplace you need the “Insight Engine” to increase your employees’ effectiveness and productivity, to help them better serve their customers while enjoying their work environment.

Sinequa has been mentioned next to Apple, IBM and the likes in the latest Gartner’s Hype Cycle Content Management/Digital Workplace 2015 Reports – for proactive search capabilities that are mandatory for a transition to Digital Workplaces.

Take a look at our presentation in the Gartner Digital Workplace Summit last September in London:

 “The Re-Imagined Digital Workplace: Where is the Beef?

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4 Ways Big Data Analytics Transform Intelligence Data into Actionable Insights

Intelligence and law enforcement agencies experience an enormous pressure to identify threats across multiple data sources. These Defense and Security organizations require real-time information at their fingertips for quick analysis and decision making.

Big Data Search and Analytics for Defense and Security

Big Data Search and Analytics for Defense and Security

Here are 4 ways Big Data Analytics can transform intelligence data into actionable insights:

  • Monitoring of Social Media interactions

Intelligence agencies must anticipate any kind of cybercrime and attacks. Social media monitoring enables them to collect and analyze relevant and targeted information relating to counter-terrorism and criminal networks. Reacting at the right time is a major challenge for these organizations that use OSINT (Open-Source Intelligence) to find, select and acquire information from various sources online (social networks, forums, blogs, websites, videos etc.) in order to get real-time insight on potential threats, generate reports and prevent any kind of attacks. In response to this challenge, intelligence agencies must invest in a cutting-edge technology that brings together data search and collection across multiple online sources and a deep content analytics of unstructured textual data that are flooding the web.

  • Detection of money laundering, fraud & terrorist financing

Money laundering is a key component of most organized crime. Terrorist networks continue to be funded through money laundering schemes that need to be identified. A powerful Big Data Search and Analytics platform enables agents to pinpoint suspect money transfers, accounts and networks of individuals involved in sophisticated money laundering schemes through a highly dynamic approach to relationship mapping.

  • Identify and correlate threats & cyberattacks

Investigators face the daunting task to accurately identify fraud and cyberattacks across big data volumes within shrinking windows of time. To prevent threats and cyberattacks before they happen, intelligence agencies must be able to deliver dynamic relationship mapping to connect people, bank accounts, credit card numbers, financial transactions, and many other data types. They need a scalable platform based on advanced Search and Natural Language Processing capabilities. Analysts uncover patterns in behavior using a combination of interactive charts, timeline analyses, tables and relationship maps.

  • Solve crime cases with powerful search capabilities

Law enforcement professionals need effective crime analysis tools to easily reveal networks of criminal activity. The sophistication of criminal behavior has increased across virtually all areas, including cybercrime, identity theft, gang activity, fraud and narcotics. These tools must provide the ability to search and analyze a wide range of sources of both structured and unstructured data to gain meaningful insights using connections between people, phone calls, license plates, addresses, properties or other forms of data.

To learn more – please download the brochure “Sinequa for Defense and Security”.

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