Nitin Rastogi

Nitin Rastogi
Nitin is an ECM enthusiast with a passion for finding patterns and use cases for technology to solve problems.

Are We There Yet With Content Analytics and Intelligence?

Content

Artificial Intelligence is a disruptive yet intuitive technology that is shaping the Enterprise Content Management systems of the future. While ECM systems have relied on passive responses, AI is stressing the use of active responses and making workflows based on findings from the machine learning aspects of AI. The use of AI is seen as beneficial in increasing enterprise knowledge or leveraging the enterprise assets. Today ECM implementations are often faced with questions on how to boost AI or make content intelligent. This blog discusses how the ECM offering from OpenText has embraced the challenges posed by AI. Content Intelligence Services Content intelligence is the ability to provide structure for unstructured content – a process enabled by the intelligent, automated tagging and categorizing of business content for personalized delivery and easy search. Tagging and categorizing content with rich metadata makes it available for reuse across multiple initiatives such as customer and employee portals, custom applications, and personalized sites where capabilities such as precise search, easy navigation, and personalization are critical to productivity and customer satisfaction. By extending the value of content through reuse, content intelligence also cuts the costs of recreating information. However, despite the value of content intelligence for advanced searching and personalization, companies have been slow to embrace this technology because it is often perceived as complex or difficult to implement and deploy. There are multiple debates within both business and technical domains as to the most optimized manner for creating the taxonomies and hence the tags for the content. Furthermore, this technology is sometimes considered inflexible, particularly when changing market conditions or new business opportunities require changes in the way content needs to be organized. Many point solutions break down under these conditions because complex integrations with other products need to be reconfigured, resulting in re-implementation costs and valuable time wasted. OpenText Documentum Content Intelligence Services OpenText™ Documentum Content Intelligence Services help enterprises create taxonomies and tag their content automatically. OpenText Documentum CIS is a high-performance extension of the OpenText Documentum platform which automates and controls the enrichment and organization of enterprise content based on powerful information extraction, conceptual classification, business analysis and taxonomy and metadata management capabilities. It is fully integrated with the Documentum platform and thus provides an automated solution for all kind of content managed within an OpenText Documentum repository. OpenText Documentum CIS turns unstructured enterprise content into intelligent structured content with powerful, unique capabilities: OpenText Content Intelligence OpenText™ Content Intelligence is an add-on to the OpenText™ Content Suite Platform that helps increase user adoption, productivity, and management insight by accelerating the deployment of tailored applications, actionable dashboards, and reports. It bundles accelerated Tile/Widget creation, enhanced REST API, and a powerful sub-tag library with a complete set of instantly deployable and easily modifiable prebuilt reports, dashboards, and applications. OpenText Analytics OpenText™ Analytics revolutionizes analytics and reporting functions of the ECM solution. The Analytics suite enables creation of dashboards, visualizations, and analytics applications that answer vital questions across the organization. OpenText Analytics Suite comprises two deeply integrated products: Big Data Analytics and Information Hub (iHub). Working in tandem, these two products give business users, business analysts and citizen data scientists the ability  of independent data preparation, data exploration, advanced analytics and sharing and socializing the analysis results in compelling dashboards, as interactive data visualizations, or pixel perfect reports. OpenText Information Hub (iHub) OpenText™ Information Hub (iHub) is a scalable analytics and data visualization platform. iHub enables IT teams to design, deploy, and manage secure, interactive web applications, reports, and dashboards fed by multiple data sources. iHub supports high volumes of users, and its integration APIs enable embedded analytic content in any app, displayed on any device. iHub enables report developers to design, deploy, and deliver secure, interactive web applications, personalized reports, and dashboards fed by multiple data sources. With an iHub application, business users can explore data on their own with interactive capabilities such as drill-downs, filter, group, build new calculated columns. Also available: Interactive Viewer Dashboards Analytics Studio OpenText Big Data Analytics OpenText™ Big Data Analytics is an analytics software solution for business users and analysts looking for an easy, fast way to access, blend, explore and analyze data quickly without depending on IT or data experts. Advanced analytics helps businesses understand their customers, markets, and business operations as well as managing IT budgets effectively, leveraging expert resources as needed. Big Data Analytics combines an analytical columnar database that easily integrates disparate data sources, with built-in statistical techniques for profiling, mapping, clustering, forecasting, creating decision trees, classification and association rules, doing regressions, and correlations. Users can get a 360-degree view of their business, explore billions of records in seconds and apply advanced and predictive analytics techniques all in a drag-and-drop experience, with no complex data modeling or coding required. Working in an easy-to-use, visual environment business user can upload their own data, clean and enrich it, blending data from disparate sources. They can then apply pre-coded algorithms and analytical techniques to gain insights from massive data sets on the fly. OpenText has both on-premises as well as cloud versions of Big Data Analytics solutions for the customers. There are also solutions specifically tailored for certain industries: Analytics for Energy & Utilities Analytics for Public Sector Analytics for Manufacturing Analytics for Financial Services Analytics for eCommerce & Retail Analytics for Logistics & Warehouses Analytics for Telecommunications Analytics for Publishing & Media Analytics for Healthcare Analytics for Marketing Service Providers Some of the advantages of choosing OpenText Big Data Analytics are: All your data in a single view: Access huge data sets from multiple sources quickly and easily. Analyze billions of records in seconds: Leverage high-performance, real-time Big Data analysis of hundreds of tables, millions of rows, billions of records—at once—for deeper business insights. No complex data modeling: Eliminate the need for data cubes, pre-processing and modeling. Minimize data analysis-related IT workload and dependency on data scientists. Best Practices analytical techniques: Pre-built algorithms and ready to use predictive analytic techniques. Discover threats, hidden relationships, patterns, profiles and trends to make fact-based decisions. No coding required: Go from raw data to sophisticated data visualizations in minutes with a few clicks. Easy to use visuals: Analyze Big Data quickly and visually with decision trees, association rules, profiling, segmentation, Venn diagrams and more. User autonomy and self-sufficiency: Empower users without statistical backgrounds to run deep analytics with pre-packaged algorithmic functions. Automate with Workflow: String together multiple steps into one process and schedule it to run on a regular basis. For more details on the benefits of OpenText Big Data Analytics, click here Conclusion Big Data Analytics is well targeted towards specific industries and allows the business users to create rich reports by analyzing data from various data sources. The solution provides easy configuration tools for the business users and reports creators to design and develop a report quickly. At the same time, Documentum Content Intelligence Services provides a mechanism for users to mine structured and unstructured content to automatically gather information as configured by the business users. Both solutions allow easy information extraction from unstructured content and allow for readable and meaningful reports. While both solutions provide two different sides of the puzzle, there seems to be an opportunity  remaining in Information Extraction techniques and development of self-learning, intelligent algorithms to parse the unstructured data into meaningful structures. The resulting structures could then be utilized for creation of reports and providing a feedback to the users or system workflows, thus rendering the system even more intelligent. More to follow on this topic shortly.

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Artificial Intelligence (AI) and Enterprise Content Management (ECM)

AI

“When the Washington Redskins win their last home game during an election year, the incumbent party will retain the White House. If they lose, the challenging party will take the election.” “Since 1972, if the SuperBowl winner is a National Football Conference member, the S&P 500 index denotes a bullish sentiment. However, if the winner is an AFC member, the market sentiment would be bearish.” – This statement was initially discovered by Leonard Koppett, and has been found to be 80% times correct as of Jan 2017. Both of the above statements hold true for different levels of confidences. The statements are based on the correlations and the rules derived using the various data sets. Neither of the statements are completely true, but can be used to develop a general behavioral pattern. The pattern needs to be vetted using much more volume of data and if found useful, can be useful for a predictive analysis. If predictive analysis can be automated, we are entering the domain of a much talked about technology – Artificial Intelligence. Artificial Intelligence (Popularly known as AI) is an intelligence as exhibited by machines, through the process of identifying patterns, learning from them, guiding decisions and then performing cognitive functions. What we have is the essence of both statements and the value of these statements is the data to back them up. This involved tremendous amount of data analysis and refining of the algorithms to be able to reach a simple and explainable data inference. Enterprise Content Management or ECM has been known to house terabytes of data – structured and unstructured. The amount of data stored in an ECM system is highly valuable for the organization if it can be mined properly. Structured data has been used for the report creation and search purposes, but the value of the unstructured content is still untapped. AI has already been an influencer in various forms that are touched upon by ECM. This blog looks at the impacts of the advancements in machine learning and artificial intelligence to the way enterprises have looked at their ECM systems. Impact of AI on Digital Interactions Artificial Intelligence has already found its way to everyone’s home today through Amazon’s Alexa, Google Allo messenger and others. We are all hearing about the pioneers of AI with self-driving cars, self-flying drones or even sensors to monitor hospital patients. AI is everywhere. Let’s consider some of the areas where AI makes a direct impact on our interaction with the digital world. Searching Google’s search engine optimization techniques are no secret to the world today. The use of machine learning algorithms, their self-improving and refinement of the search results have already shaped the way the users interact with the web today. There are multiple adaptations to this technique – music stores which help you select the right music and make predictions on what your choices should be. Generating Content Automated Insights – the creator of Wordsmith, the world’s only public natural language generation platform and #1 producer of content in the world, stated that its software created more than a billion stories last year – many with no human intervention. Gartner estimated that 20% of business content will be authored by machines by 2018. A writer cannot go through over 2 million blog posts created daily, but can leverage the technology to find out what keywords are used in successful content, or what topics resonate best with their audience. Designing Websites Platforms like Wix or The Grid have already adopted a supervised learning way of interacting to help people create their own websites. By leading customers through a set of questions and allowing them to make choices, these platforms also make recommendations on popular themes and what would go better with the choices already made. Promoting and Propagating Content Twitter bots can make this process faster and more streamlined, enabling marketers to publish and push content automatically across the platforms they want. Automated tweets can even be set to match user moods and emoticons. Predicting Choices A lot of online music streaming services make recommendations based on AI indicators. The North Face and 1-800-Flowers use AI tools as shopping assistants – helping customers and make spot-on recommendations. Moving Conversions AI tools have also been in use by sellers like ebay and Airbnb. These tools allow the sellers to understand the latest trends and help them price their product or service accordingly. At the same time, the same tool allows the customers to draw conclusions on the prices offered to them by the sellers. Continue reading about Enterprise Content Management and how AI could transform ECM on page 2.

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Artificial Intelligence and EIM

Artifical Intelligence

During a recent visit to Los Angeles, California, I happened to stay at Residence Inn Marriott at LAX. Unable to sustain my hunger pangs in the middle of the night, I ordered some food. And I had the best, and the most surprising experience!. The food arrived quickly and was not carried by a server, but a robot – Wally! Wally is a 3 feet tall robot that moves on wheels, can be programmed for the room number and delivers to the room. More than being served by a robot, I was fascinated by the amount of information processing and intelligence built into the machine to be able to take precise turns, get on the right elevator, reach the correct floor and then the correct door number! I was later told that the number of foot falls and the room service requests have increased since Wally has been put to service. Piqued by my interest, I later found Hilton Hotels also deployed a robot “Connie” as a concierge at Hilton in McLean, VA. Connie can greet the guests and answer their questions about the services, amenities and local attractions. Named after the Hilton chain’s founder Conrad Hilton, Connie is powered by machines delivering Artificial Intelligence (AI). Robots delivering a great experience to hotel guests are an example of how Artificial Intelligence coupled with devices can perform tasks that are repeatable, process-oriented, rule-based operations.  AI works on the principle of analyzing data, identifying patterns and turning data into information that may be useful in decision making. This form of AI has been very popular and has been in existence for a long time. Its populist nature and long term existence stems from the underlying principle that it is rules based and can only predict from a fixed set of probably outcomes, based on the information already provided. This form of AI was initially seen in 1997 when IBM’s Deep Blue won a game against Garry Kasparov – Chess Grand Master. Though the computer was retired soon after, the concept of a machine adapting to a large set of rules and able to make decisions became a reality. Later, Apple’s Siri, Google’s Google Now, Microsoft’s Cortana and Amazon’s Alexa enhanced the powers of AI and entered our daily lives. This form of intelligence which is primarily ability to compute is known as Applied AI or Weak AI or Narrow AI. This is developed quickly to solve a purpose. Amazon, Apple, Google, Microsoft have yet not ended their quest in being your own personal assistant. They are aiming to be able to understand your emotions when you talk to them, which requires a context in which the data is provided to them. And with this, they want to develop the ability to be able to negotiate decisions for you. Tesla and Google have already tried to take it to the next level by releasing autonomous auto driving software and devices. AI in the true sense. This form of AI is known as the General Purpose Artificial Intelligence. AI is exciting and is growing in presence and applications every day. The stories from Sci-Fi are becoming reality sooner than later. However, at the heart of its growth lies the importance of abundance of data. Data that can be managed, mined, analyzed and processed to get information. Enterprise Information Management has an important role to play in the growth of AI in enterprises. With its ability to store, manage and present data, EIM is only bridging the gap today.

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