Cognitive Search & Analytics Transforms The Enterprise From Data Driven to Information Driven

**This article was originally published on AI Business magazine**Sinequa-AI-for-Business

The quest for actionable insights and answers from within vast troves of data is neverending within the modern enterprise. There’s good reason for that – it is the end goal of all information work – but the process is anything but optimized. Global analytics firm Forrester revealed as much in a 2017 report, which found that more than 54% of global information workers are interrupted from their work a few times or more per month by time wasted trying to gain access to information, insights, and answers.

It’s a problem that goes far beyond the limitations of conventional enterprise search technology – it’s a Sisyphean challenge, thanks to the sheer volume of data being created every single second.

“As organizations in data-intensive industries strive to create value, enhance customer experiences, and differentiate themselves from their competition, they are placing demands on their knowledge workers in unprecedented ways,” explains Laurent Fanichet, VP of Marketing for Sinequa. “Frequently, the data and knowledge they are looking for is isolated, segmented, and fractured. It’s difficult to surface the right information at the right time to see the patterns in the data.”

Fanichet has a clear grasp on the key problem Sinequa, an independent software vendor specialising in cognitive search & analytics, is trying to address. In its recent report, Forrester Wave: Cognitive Search and Knowledge Discovery Solutions, (Q2 2017), Forrester defines cognitive search as ‘the new generation of enterprise search that employs AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content’ – and, in the same report, go on to highlight Sinequa for the applications of their NLP technology in enterprise search.

The kind of cognitive search and analytics platform Sinequa offers, Fanichet explains, refers to an information system that is capable of automatically extracting relevant insights from diverse enterprise datasets for users within a specific work context. “Cognitive search brings the power of AI to enterprise search,” he says. “It helps organizations in data-intensive industries to become information driven.”

A recent IBM Watson report highlights the applications of cognitive search in the aerospace sector. One company uses these enhanced search capabilities “to improve supply chain visibility and reduce cycle time, saving millions of dollars on critical parts deliveries.” Furthermore, the system enables aircraft technicians to search through “reams” of maintenance records and technical documentation. “Now, if a worker needs to know what’s causing high hydraulic oil temperatures, the [cognitive solution] identifies historical cases with similar circumstances, finding patterns that point to the root cause of the overheating.” The report goes on to note that the solution in question saves the airline manufacturer up to $36 million per year.

Cognitive search and analytics likewise has its applications in the health and pharma sector. AI Business recently spoke to Karenann Terrell, GlaxoSmithKline’s first ever Chief Data and Analytics Officer, and former CIO of Walmart. She explained that a big component of what it takes to develop medicine can benefit from next-generation computing and machine learning. “Approximately 1/3 of the total cost of developing a medicine (>$2.5bn) is spent during the time it takes from identifying your target (the process in the body that you want to affect) to testing your molecule in humans for the first time,” she explained. “This process can take around five years. [GSK’s] goal with artificial intelligence is to reduce this time to just one year in future.”

“These are just a few of the many business areas where surfacing the information from within their data can drive better decisions,” Fanichet argues. He explains that cognitive search and analytics also have a range of powerful potential applications within customer service, enabling organizations to:

  • Provide personalized and highly relevant communication to their customers
  • Nurture customer relationships and prevent customer churn
  • Improve productivity, reduce operating expenses, and gain operational efficiencies
  • Minimize customer service representative turnover and knowledge loss

The Challenges Ahead for Cognitive Search

The potential use cases speak for themselves, but that doesn’t mean there aren’t challenges ahead for enterprises looking to incorporate cognitive search technology into their work. While working with clients, Fanichet explains, Sinequa helps them to understand that there are a set of common machine learning challenges along the path ahead. Expertise is often the first hurdle, but he maintains that there are many different types of AI implementation challenge. “Assuming that enterprises are able to resolve a dearth of expertise, there are still other challenges – most of which are specific to the type of AI being pursued.”

Take supervised machine learning, where the system learns to recognize patterns by observing ‘correct’ patterns provided by humans. “The greatest challenge is around providing sufficiently labelled training datasets from which the system can learn,” Fanichet explains. This is something Matt Buskell highlights in his ‘10 keys to AI implementation‘, recommending that following the initial loading of data and knowledge base, the system needs to go through a phase of refinement once the software has launched. “During this phase, things like gain and variance for Machine Learning, or intent training for NLP and maybe model refinement to cognitive reasoning need to be improved. During this phase, it is essential to carefully release the software and measure how well it’s performing over a 6-12 week period, at the least.”

Fanichet likewise highlights the obstacles unique to unsupervised machine learning, in which the system identifies existing patterns and a human determines their usefulness. “The greatest challenge is balancing the system’s need for sufficient data with the proper human guidance and interpretation needed to train the system,” Fanichet argues. This is as much an issue of skills and process culture as it is technical expertise, and is reflected in a recent Genpact survey of over 300 senior executives, which argues “AI cannot be implemented piecemeal. It must be part of the organisation’s overall business plan, along with aligned resources, structures, and processes.” Collaboration is therefore key.

Finally, there’s a need to formulate clear goals and outcomes, Fanichet says. “When pursuing reinforcement learning, where the system makes many attempts and learns from the outcome to take better actions, the greatest challenge is providing the system with a defined goal and sufficient practice in a dynamic environment so that the system can effectively learn from trial and error.”

With Sinequa, researchers, designers and engineers have immediate access to all the information needed to work productively.

With Sinequa, researchers, designers and engineers have immediate access to all the information needed to work productively.

Sinequa Brings the Power of AI to Enterprise Search

Fanichet believes Sinequa offer a range of unique intelligent capabilities within the analytics space:

  • Robust Indexing Engine: “If cognitive search was all about matching a keyword, a single index would suffice. The best results are obtained when multiple indexes are combined, each providing a different perspective or emphasis, providing a comprehensive overview of the information available. This provides the best possible understanding of the meaning it carries.”
  • Enterprise Grade: “Sinequa was designed from the start to support the complexities and multiple security layers of today’s enterprises. It was also designed to be immersed in diverse enterprise environments and can operate within the context of a specific industry and the language of the specific organization.”
  • Topically Aware: “Connecting information along topical lines across all repositories surfaces the collective expertise of the organization and makes it transparent. This is especially valuable in large organizations that are geographically distributed. By connecting people with expertise, the overall responsiveness of the organization increases.”
  • Natural Language Processing: “Sinequa’s world-class NLP offers automated language detection; lexical and syntactical analysis; and automatic extraction of dozens of entity types, including concepts and named entities like people, places, companies, etc. It also supports text mining agenda that is integrated into the indexing engine. This enables the extraction of virtually any function, relationship, or complex concept from the content.”
  • Machine Learning: “Sinequa leverages ML to enhance and improve search results and relevancy. This is done during ingestion but also constantly in the background as humans interact with Sinequa. It has become an essential part of the platform since it can handle complexity beyond what’s possible with rules.”
  • Well Designed User Experience: “Sinequa’s front-end serves as an intelligent agent that employees can consult for institutional knowledge that can be readily applied to the task or situation at hand. The experience is well designed in the sense that it is aesthetically pleasing, it is understandable in that it makes use of the user’s intuition, it is unobtrusive, and perhaps most importantly, it is contextual to the user’s goals.”
  • Ubiquitous Connectivity: “Sinequa’s product comes with over 160 ready-to-use connectors, all of which were developed in-house, thus ensuring consistency, quality control, and high performance.”

 

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5 Ways Information-Driven Companies Optimize Sales

This article was originally published on RT Insights.

Teamwork and corporate profitStreamlining sales and customer data eliminates the burden learning and mastering multiple applications — increasing agility and reducing operating expenses.

In an increasingly sophisticated economy where customers are inundated with options, sales forecasting is challenging, and achieving revenue goals is even harder.

Sales teams are constantly trying to identify lucrative target markets, close more deals and shorten sales cycles. Companies must become information-driven by equipping their sales team to be nimble, efficient and knowledgeable to focus on sales opportunities with the highest chance of success. Whether it’s lead generation, ecommerce or direct sales, sales teams need the power of relevant and timely information more than ever.

Access to information isn’t enough to optimize sales

With information in the typical global enterprise scattered across a growing digital landscape, including CRM, ERP and myriad internal and external repositories and applications, harnessing it can be a tremendous challenge. Mere access to this information is pointless if it is not timely and relevant. Successful information-driven organizations have learned how to address this issue, fueling sales productivity and increasing revenues as a result.

Every sales leader, regardless industry, faces these challenges:

  • Increase average deal size and drive top line revenue.
  • Shorten sales cycles and increase close rates.
  • Increase the number of net new customers.
  • Capture as much business as possible from existing customers.
  • Train new reps to become effective in their new roles as quickly as possible.

While high-performing corporations expect their sales teams to accomplish the following:

  • Maximize contract value and increase revenues.
  • Make informed strategic decisions.
  • Anticipate and respond faster to customer needs.
  • Create a thriving business based on thorough understanding of key clients.
  • Know what markets to target and who the players are within an organization.
  • Fuel higher operational efficiencies.

5 ways information gives you a competitive advantage

With these challenges and expectations in mind, here are five examples of how information-driven sales teams are leveraging modern data analytics technologies to improve their effectiveness and creating distinctive competitive differentiation for their organizations:

  1. Seamlessly aggregating and integrating all the company’s diverse data repositories toward delivering relevant, real-time information to sales teams around the world.
  2. Providing a comprehensive view of every customer interaction within their organization from a single access point, even if the basic data is stored in separate systems and databases. This helps maximize contract value by providing sales professionals with the visibility to better understand the customer’s overall needs in order to customize offers and services.
  3. Delivering unified information at both the contact and company level to enable information-driven sales teams to prioritize where they spend their time and energy to develop better relationships with their prospects. This includes the business drivers of senior leadership, the latest public financial information, changes in key management, buying behaviors relevant to cross-selling other products and more.
  4. Contextualizing information by product or by territory. Based on a sales group or individual profile, the information is automatically filtered by product and/or territory assignment.
  5. Enabling easy collaboration and knowledge-sharing uniformly across disparate silos of information. This promotes knowledge transfer among sales reps, helps surface important content, simplifies training and reduces the learning curve as new hires get up to speed quickly.

Optimize sales data for real cost and time savings

Eliminating the need to navigate multiple systems and databases to find information simplifies the sales process and creates a highly productive and efficient environment where sales professionals thrive. This translates to real cost and time savings.

Take technology vendors, for example, a group that Forrester Research found spends close to 20 percent of their selling, general and administrative (SG&A) costs — more than $135,000 per quota-carrying salesperson — on support-related activities.

By streamlining sales and customer data, information-driven sales organizations eliminate the burden and time consumption of learning, retention and mastery of multiple applications, thereby increasing agility and reducing operating expenses. This creates a critical competitive differentiator as it frees up sales teams to elevate their performance toward maximizing contract values, making informed strategic decisions and responding faster to client needs.

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Sinequa Helps Box Customers To Be Information-Driven

noiseMany customers that use Box for cloud content management are typically large, geographically distributed organizations. The four scenarios below describe common ways that Sinequa helps these customers leverage their enterprise information to become information-driven.

Increase the Signal, Decrease the Noise
Customers who have migrated even a portion of their enterprise content to Box have made a significant step.  Workers in their organization can no doubt share and collaborate more easily than ever before; they no doubt have reduced email overhead; and they are probably working the way they want to given all of the friendly integrations with Box, including Outlook, Office365, Google Docs and the like.   However, being in the cloud does not automatically mean the valuable “signals” in your data rise above the “noise”.  Messy data migrated to the cloud is still messy data.  Sinequa helps workers quickly narrow in on the information and insights necessary to do their job effectively and with confidence.  By analyzing the content and enriching it using natural language processing and machine learning algorithms, Box users can quickly find the information and insights they need to be effective and responsive.

Connect Data

connect-data

Many Box customers run their business with other enterprise applications and information repositories, all of which contain data and content related to the information
stored in Box.  Sinequa brings advanced analytics and cognitive techniques to “connect” the data and bring context across all of the various enterprise sources, whether they be in the cloud or on premise.  By connecting the data, knowledge workers can better navigate and see how the data and connect fit together along topical lines, regardless of how many repositories make up the enterprise information landscape.

Identify Knowledge & Expertise

Screen Shot 2017-10-13 at 2.40.37 PMAs previously mentioned, many Box customers are large (or even very large) geographically distributed organizations with expertise in a wide variety of subject matter areas.  In these organizations, specific experts are difficult to identify given the size and distributed nature of the organization.  This is a modern problem that requires a modern solution.  As users store content and collaborate within Box, Sinequa’s advanced cognitive capabilities analyze that content to determine not only the areas of expertise across the organization but who the specific experts are and surfaces that information to end users.  This connects people across geographic and departmental boundaries, accelerating innovation and elevating the performance of the overall organization.

Leverage 360º Views

Screen Shot 2017-10-13 at 2.42.23 PM

Think of all the “entities” that are critical to Box customers running their business.  These business entities include customers, either specific individuals (B2C) or accounts (B2B), products, parts, drugs, diseases, financial securities, regulations, etc.  Having all of the enterprise data virtually connected by Sinequa makes it possibly to provide a unified “360º View” of these various entities to bring all of the right information to the right person at the right time.
As you can see, leveraging Sinequa to contextualize the information within Box and other enterprise repositories not only boosts productivity and keeps knowledge workers in the flow but has repeatedly proven to enhance customer service, improve regulatory compliance and increase revenue within different areas of the business.  Achieving these benefits positively impacts the bottom line and serves as validation that an organization has become truly information-driven.
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4 Ways Real-Time Data Improves Customer Service

This article was originally published on RT Insights.

Instant access and 360-degree views of all customer and product data is mandatory to enable customer service representatives to operate more efficiently.

Customer Service

Customer service centers serve as organizational information hubs, resonating with the voices of the customers. They are strategic to an enterprise, as they are often the most recent and most frequent point of contact that the customer has with an organization.

Properly used, customer service centers can satisfy customers and improve retention. They can also drive revenue by cross-selling and upselling. To do this, they must manage the volume of interactions efficiently and control average handle time (AHT). Increasingly, they must achieve this with tighter budgets. Instant access and 360-degree views of all customer and product data is mandatory to enable customer service representatives (CSRs) to operate more efficiently.

With people and information spread across various locations, this task can seem daunting. The right mix of technology can enable customer service centers to overcome these challenges and run at peak performance. Below are four tips for CSRs to manage high volume of interactions:

Improving visibility into real-time customer data

CSRs need visibility into customer data across all contact and interaction points within the enterprise — regardless of location, repository and format. By aggregating all data and providing a single, secure access point to relevant and real-time customer and product information, a unified view of information can be formed to help CSRs respond to customers’ concerns and issues quickly and accurately.

Relieved of the burden of navigating multiple applications to find a single piece of relevant information, CSRs can immediately concentrate on the callers’ concerns and quickly resolve their issues — increasing first call resolution and reducing average handle time to minimize the volume of customer interactions. Automatically providing a unified view of customer information effectively enables the customer service center to improve productivity and reduce operating expenses.

Automating access to relevant information

High attrition has always been a major concern for customer service center managers. Rehiring and retraining costs directly impact the bottom line. More importantly, high turnover rates burden CSRs, affect productivity and hamper the customer service center’s ability to provide quality service.

Automating access to relevant information can help customer service centers lower attrition by minimizing the excessive pressure and stress of the customer service center environment, which is cited as a major reason for attrition.

Leveraging automated analytics on top of customer and product information, customer service center managers can quickly spotlight new products for training and push information out to their CSRs. Simplifying the way CSRs access customer and product information and providing ways for CSRs to easily collaborate and share knowledge reduces CSR stress and consequently turnover. When CSRs have the information needed to answer customer questions and resolve issues confidently, they are much better able to interact comfortably and build close and lasting customer relationships.

Accelerating time to proficiency

CSRs never know what inquiry or problem they will face on the other side of an inbound call. As such, they must be well-versed on the products, services and policies of their organization. Successfully training CSRs is vital to the success of the customer service center. The cost of attrition per CSR is high, with new employees taking up to three months to complete initial training in many industries.

This can be exacerbated as many customer service centers have myriad applications and repositories, such as CRMs, ERPs and external databases, that CSRs must learn to navigate to prepare for and complete a call. The ability to seamlessly connect to these applications and provide a unified view to information greatly reduces training time and cost.

Sharing CSR knowledge

Collaboration capabilities that promote knowledge sharing and retention — even if employees leave — enable the remaining CSRs to maximize and enrich each customer interaction. Enterprise data is continually growing; as a result, CSRs have even more information to learn and retain. In addition, customer service centers are often scattered across far-reaching locations without sufficient support for their distributed organization. A scalable, distributed platform for information access solves this problem and allows data to grow without compromising access or speed for CSRs. They can then concentrate on listening to customer concerns and ensuring complete satisfaction, enhancing the entire customer experience.

Companies that employ the right mix of technology in their customer service centers empower their CSRs to go beyond solving customer issues to being customer champions — listening and responding fittingly to their needs.  By actively listening, CSRs can turn complaints into revenue. By having relevant information consistently and securely available, organizations can react quickly to customer demands, innovate business processes, profile new target markets and formulate ideas for new product features.

Consolidating silos and promoting the quick and easy transfer of information and insight captured in the customer service center across the entire enterprise allows executives to make informed decisions that positively impact the direction of the company.

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Cognitive Search Brings the Power of AI to Enterprise Search

Forrester, one of the leading analyst firms, defines Cognitive Search in a recent report¹ as: The new generation of enterprise search that employs AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources. Here is a shorter version, easy to memorize: Cognitive Search = Search + NLP + AI/ML
Of course, “search” in this equation is not the old keyword search but high-performance search integrating different kinds of analytics. Natural Language Processing (NLP) is not just statistical treatment of languages but comprises deep linguistic and semantic analysis. And AI is not just “sprinkled” on an old search framework but part of an integrated, scalable, end-to-end architecture.

AI Needs Data, Lots of Data
For AI and ML algorithms to work well, they need to be fed with as much data you can get at. A cognitive search platform must access the vast majority of data sources of an enterprise: internal and external data of all types, data on premises and in the cloud. Hence the system must be highly scalable.

Continuous Enrichment
Cognitive Search uses NLP and machine learning to accumulate knowledge about structured and unstructured data and about user preferences and behavior. That is how users get ever more relevant information in their work context. To accumulate knowledge, a cognitive search platform needs a repository for this knowledge. We call that a “Logical Data Warehouse” (LDW).

The Strength of Combination
To produce the best possible results, the different analytical methods must be combined, not just executed in isolation of each other. For example, machine learning algorithms deliver much better results much faster if they work on textual data for which linguistic and semantic analyses have already extracted concepts and relationships between concepts.

Whitepaper-kmworld-07-2017Get your copy of the full paper here and learn more about current use cases of cognitive search and AI at large information-driven companies.

(1) Forrester Wave: Cognitive Search & Knowledge Discovery Solutions, Q2 2017
Read the full report on https://www.sinequa.com/forrester-wave-2017/

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