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

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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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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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The Heat in The Trend Point: June 24 to June 28

We come across so many articles in the media that point to next generation search solutions and innovative business intelligence systems, but there are many companies still using the technology of bygone days. This idea occupied  The Trend Point over the past week.

Despite recent innovations with semantic search capabilities and interface design that tends towards an intuitive user experience, legacy systems still remain in the enterprise. “Enterprise User Experience Matters” summarizes the state of the matter:

The computational legacy of the 1960s is still with us today, despite a surplus of aluminum and gorilla glass. And despite the aspirations being fulfilled on the consumer level, a comprehensive simplicity is lacking at the core of most enterprise software. Obscurity and inconsistency reign (think of BlackBerry’s descent of late) where transparency and interoperability ought to go hand in hand. The egregious result is that the everyday tools, the interfaces that we must interact with daily in our jobs—from banker to lawyer, from journalist to physician—are almost incapable of leveraging the considerable network of information that many of us need to wade through at work.

When the problem has been recognized as an information management issue stemming from the software “solution,” many companies know they must take action. However, there is no one correct path to take. We saw the following summary in “Data Management Tips” offer advice:

Keep in mind that overhauling an existing system or syncing all of the databases in an organization can be an enormous, costly, and difficult project that can take months or years to implement – this may make it impractical, particularly if other projects will deliver a bigger business benefit. However, you can take other steps to improve data management for your team, and for your organization.

What should be done with existing data when replacing a legacy storage system? “Combining Big Data with Existing Data” calls for the integration of data previously collected and stored with the huge chunks of unstructured data represented by varying file types. The following information was relayed in this post:

Big data opens an entirely new data universe to consider and use to improve decision making. But how does a business/systems analyst turn it into actual usable data so that it can be used for operational improvements that result in real business value? Success depends on how fast and seamlessly you can combine your big data with your enterprise data and present that collective information to your decision makers.

While we definitely recommend storing and parsing old data in addition to new data, merging legacy enterprise data warehousing systems with new solutions is not always a cut and dry answer. When there are many search solutions that provide efficient information access in real-time, who needs to hold on to any remaining parts of a legacy search system? Companies like Siemens, for example, are choosing to replace their out-dated search technology with Unified Information Access.

Jane Smith, July 03, 2013

Sponsored by ArnoldIT.com, developer of Beyond Search

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The Heat in The Trend Point: June 10 to June 14

Big data is usually mentioned at least a bit in The Trend Point, and last week was no exception. We noticed that many of the articles seemed to be pointing towards going beyond information retrieval.

Value must be added through the technologies of an information management, search and analytics system. An article quoted in “Big Data without Value is Just a Lot of Data” states the following:

Relying solely on the information gathered by Big Data is like watching a group of people from a relatively far distance. It’s possible to see what they’re doing while they interact with each other and engage in conversations, but it’s virtually impossible to understand why they’re holding those conversations, what are they feeling that drives their actions, what is the emotion underpinning those conversations, and most importantly, how they’ll determine the future behaviour of each individual and the group at large.

We heard a similar sentiment repeated in “Data Visualization Key to Data Understanding” with an emphasis on the end goal being easy access to actionable information. This post relayed the following:

It’s typical for an analyst who has been working on a project for more than two months to show all the frequency or statistical results with a presentation deck consisting of hundreds of slides. Stop! A few charts with great data visualization are worth 1,000 slides. Actionable visualizations such as Price or Attrition Alerts can help sales teams better engage with customers instead of analyzing a plethora of reports. The key: reports should be easy to understand as well as recommend the next actionable step for business leaders.

In another post, we saw another mumbling that big data is a misnomer better represented as big content. We noted some of the thoughts that followed — the necessity of extracting value from unstructured content — in the article “Big Data or Big Content“:

Unstructured content is often included almost as an afterthought, with extraction and enrichment applied on-the-fly, from scratch on a case-by-case basis. This undermines the potential of Big Data in several ways. It raises the cost of incorporating unstructured content while also increasing the opportunities for the introduction of inconsistencies and errors reducing the quality of the final product. Most importantly, the ad hoc approach also reduces the potential of Big Data by obscuring the extent of available raw materials.

It is refreshing to see that these several media sources are no longer discussing simply mashing up raw data from different sources. The important pieces are fusion of data (both structured and unstructured) and that comes through strong analytics that can detect what belongs to the same semantic category. Then a system like Unified Information Access from Sinequa can “fuse” results with other data, like geographic position or customer history, and others.

Jane Smith, June 19, 2013

Sponsored by ArnoldIT.com, developer of Beyond Search

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The Heat in The Trend Point: May 20 to May 24

In The Trend Point over the past week we have seen rumblings about business intelligence solutions of the past and present and the push many companies are making to deliver solutions based around an efficient and intuitive user experience.

The need for solutions with an easy to use interface is clear. Echoing this sentiment, the post “Business Intelligence Bolstered with Semantic Capabilities” quotes the following from a recent Wired article:

One of the key obstacles in bringing intelligence to BI is interpreting the vast store of data correctly and harvesting the pertinent business ‘stories.’ The end user must understand the context of their data to look for relevant events. A meaningful analytics solution helps users identify actionable business insights rather than generate more reports that lead to ‘paralysis by analysis.’

The trust developed between a user and an app is a sacred bond. The article “BI Solutions Need To Address End Users and Analysts Needs” calls for more enterprise oriented apps to be such trusted sources of information. The following information was relayed in this post:

…knowledge workers suffer not only from information overload, but also from functionality overload. End-users are not analysts. When individuals need to check the weather, they do not perform a detailed analysis of the weather patterns. They trust what the weather app says. Similarly, business users want apps that deliver them the trusted information they need to do their jobs. From this perspective, the consumerization of BI can only be driven by technologies that turn the classic enterprise BI portal into a BI app store, where end users can go and select targeted, specific apps that address their concrete questions.

In “Intelligent Business More than just Business Intelligence we saw the following summary about where BI is headed:

Business intelligence is passé. Now it’s the intelligent business, and this shift is more than a simple name flip…This flip from data-driven decisions beginning inside your company and pushed to the outside world to outside data happening in real time being the driver of your company’s inside operations means big changes for the traditional business intelligence and business analysis vendors. And, of course, those changes present opportunity for the intelligence upstarts.

Traditional BI is not propelling businesses into success as it completely misses the mark on unstructured data. However, solutions like Sinequa’s Unified Information Access extends the view of classical BI to unstructured data, thus helping to make better use of existing reports on all levels of management. Moreover, this technology utilizes nifty linguistic and semantic analysis features that produce structures in the masses of unstructured data. Now that sounds like the makings of a system for an intelligent business.

Jane Smith, May 29, 2013

Sponsored by ArnoldIT.com, developer of Beyond Search

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