Mark Gamble

Mark is a Senior Director in Technical Marketing for OpenText Analytics.

Enterprise World: Analytics Workshop Takes You From Zero to Power User in 3 Hours

Analytics Workshop

One of the great things about OpenText™ Analytics Suite is its ease of use. In less than three hours, you can go from being an absolute beginner to creating dynamic, interactive, visually appealing reports and dashboards. That’s even enough time to become a “citizen data scientist,” using the advanced functionalities of our Analytics Suite to perform sophisticated market segmentation and make predictions of likely outcomes and customer behavior. So by popular demand, we’re bringing back our Hands-On Analytics Workshop at Enterprise World 2017, July 10-13 in Toronto. The workshop comprises three 50-minute sessions on Tuesday afternoon, July 11. Just bring your laptop, connect to our server, and get started with a personalized learning experience. You can attend the sessions individually – but for the full experience, you’ll want to attend all three. Learn how businesses and nonprofits use OpenText Analytics to better engage customers, improve process and modernize their operations by providing self-service analytics to a wide range of users across a variety of use cases. This three-part workshop is also valuable for users of OpenText™ Process Suite, Experience Suite, Content Suite, and Business Network. Here’s what to expect in each segment: 1. ANA-200: Learning the Basics of OpenText Analytics Suite This demo-packed session serves as an introduction to the series, and will arm you with all you need to know about the OpenText Analytics Suite, including use cases, benefits and customer successes, as well as a deep dive into product features and functionality. Through a series of sample application demonstrations, you will learn how OpenText Analytics can meet any analysis requirement or use case, including yours! This session serves as a perfect lead-in for the next 2 sessions: ANA-201 and ANA-202. 2. ANA-201 Hands-On Workshop: Using Customer Analytics to Improve Engagement This hands-on session will introduce the advanced and predictive analysis features of the Analytics Suite by walking you through a customer analysis scenario using live product. Connect from your own laptop to our server and begin segmenting customer demographics, discovering cross-sell opportunities and predicting customer behavior, all in minutes – no expertise needed in data science or statistics. You will learn how OpenText Analytics can provide valuable insights into customers, processes and operations, improving how you engage and do business. 3. ANA-202 Hands-On Workshop: Working with Dashboards to Empower Your Business Users This hands-on session will introduce the dashboarding and reporting features of OpenText Analytics by walking you through a self-service scenario where you create and share dashboards completely from scratch. Connect from your laptop to our server and see just how easy it is to assemble interactive data visualizations that allow users to filter and pivot the information any way they wish, in just a matter of minutes! You will learn how OpenText makes it easy for any user to analyze and share information, regardless of their technical skill. Of course, we have plenty of other interesting sessions about OpenText Analytics planned for Enterprise World. Get a sneak peek at product road maps, exciting new features (including developments in Magellan, our cognitive software platform), and innovative customer use cases for the OpenText Analytics Suite. Plus, get tips from experts, immerse yourself in technical details, and network with peers, and enjoy great entertainment. Click here for more details about attending Enterprise World. See you in Toronto!

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Big Data Is Still a Game Changer, but the Game has Changed. Here’s How.

Not long ago, organizations bragged about the large volume of data in their databases. The implied message from IT leaders who boasted about their terabytes and petabytes and exabytes was that company data was like a mountain of gold ore, waiting to be refined. The more ore they had, the more gold – that is, business value – they could get out of it. But the “bigness” of Big Data isn’t the game changer anymore. The real competitive advantage from Big Data lies in two areas: how you use the data, and how you provide access to the data. The way you address both of those goals can make or break an application – and, in some cases, even make or break your entire organization. Allow me to explain why, and tell you what you can do about it – because mastering this important change is vital to enabling the digital world. How Big Data Has Changed Each of us – and the devices we carry, wear, drive, and use every day – generate a surge of data. This information is different from Big Data of just a few years ago, because today’s data is both about us and created by us. Websites, phones, tablets, wearables and even cars are constantly collecting and transmitting data – our vital stats, location, shopping habits, schedules, contacts, you name it. Companies salivate over this smorgasbord of Big Data because they know that harnessing it is key to business success. They want to analyze this data to predict customer behavior and likely outcomes, which should enable them to sell better (and, of course, sell more) to us. That’s the “how you use data” part of the equation – the part that has remained pretty consistent since market research was invented more than 100 years ago, but that has improved greatly (both in speed and precision) with the advent of analytics software. Then comes the “how you provide access to data” part of the equation – the part that highlights how today’s user-generated Big Data is different. Smart, customer-obsessed businesses understand that the data relationship with their consumers is a two-way street. They know that there is tremendous value in providing individuals with direct, secure access to their own data, often through the use of embedded analytics. Put another way: the consumers created the data, and they want it back. Why else do you think financial institutions tout how easily you can check balances and complete transactions on smartphones, and healthcare companies boast about enabling you to check test results and schedule appointments online? Making your data instantly available to you – and only to you – builds trust and loyalty, and deepens the bond between businesses and consumers. And like I said earlier, doing so is vital to enabling the digital world. The New Keys to Success But when a business decides to enable customers to access their data online and explore it with embedded analytics, that business must give top priority to customers’ security and privacy concerns. In a blog post, “Privacy Professor” Rebecca Herold notes that data breaches, anonymization and discrimination rank among the Top 10 Big Data Analytics Privacy Problems. Her post is a must-read for organizations that plan to provide data analytics to customers. To underline Herold’s point, Bank Info Security says that personal data for more than 391.5 million people was compromised in the top six security breach incidents in 2014 – and that number does not include the Sony breach that made headlines. Security and privacy must be a primary consideration for any organization harnessing Big Data analytics. Remember what Uncle Ben said to Peter Parker: “With great power comes great responsibility.” Meeting the privacy and security challenges of today’s user-generated Big Data requires a comprehensive approach that spans the lifecycle of customer data, from generation through distribution. If you want guidance in creating such an approach, check out the replay of a webinar I presented on June 23, Analytics in a Secure World. My colleague Katharina Streater and I discussed: The drivers and trends in the market What top businesses today do to ensure Big Data protection How you can secure data during content generation, access, manipulation and distribution Strategies for complying with data security regulations in any industry If you watch the replay, you’ll come away with great ideas for securing data from the point of access all the way through to deployment and display of analytic results. We explained why a comprehensive approach minimizes the risk of security breaches, while simultaneously providing a personalized data experience for each individual user. We closed the program by explaining how OpenText Analytics and Reporting products have the horsepower required to handle immense volumes of data securely. We showed how the OpenText Analytics platform scales to serve millions of users, and explained why its industrial-strength security can integrate directly into any existing infrastructure. Please check out Analytics in a Secure World today. Privacy Please image by Josh Hallett, via Flickr.

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Wearables, Big Data, and Analytics in Healthcare

As wearable technology – including smartwatches, fitness trackers, and even clothing and shoes with integrated sensors – moves into the mainstream, healthcare organizations are exploring ways to use these devices to simplify, transform and accelerate patient-centric care. Their goals include boosting people’s health, improving patient outcomes, streamlining manual processes and opening new avenues for medical research and epidemiology. Analytics, data visualization and reporting are central to those efforts. Transforming Data into Insight and Action Wearables today can monitor and gather wearers’ activity level, heart rate, and other vital signs; reward wearers for healthy activities and habits; and alert the wearer and others, such as doctors, emergency responders and family members when problems arise. “Wearable health technology brings three distinctly beneficial trends to the table – connected information, community, and gamification,” writes Vala Afshar on the Huffington Post. “By harnessing this trifecta, healthcare leaders have new ways to build engagement and create accurate, far-reaching views of both personal and population health.” Wearables are both producers of data (collecting and transmitting wearers’ data) and consumers of data, receiving and displaying information about the wearer’s well-being and progress. Wearables are textbook generators of big data, with high velocity, volume and variety. And as in any big data scenario, transforming that data into insight and action requires a powerful, scalable analytics, data visualization and reporting platform. Wearables in healthcare share many characteristics with the networks of sensors in Internet of Things (IoT) applications. But healthcare adds additional complexities and wrinkles, particularly regarding security. With IoT, everyone agrees that security is important, but the rules and standards vary and are subject to debate. However, when individuals’ personal health data is in the mix, more (and more complicated) laws, security regulations and privacy concerns kick in. “A person’s health information is particularly sensitive,” writes Victoria Horderen in the Chronicle of Data Protection. “[B]oth in a legal sense (because health information is categorized as sensitive under EU data protection law) but also in an obviously everyday sense – people feel that their health information (in most but not all circumstances) is private.” Horderen writes specifically about the EU Data Protection Regulation, but the points she makes apply globally. The takeaway, I think, is that a platform supporting a wearable initiative in healthcare requires a robust, proven security foundation. Many Use Cases With a flexible big data platform supporting wearables, many healthcare use cases arise. Most of these are possible with today’s technology, while others could be on the horizon using future generations of devices. Some use cases include: A person under observation for heart disease can use a wearable to monitor his or her heart rate 24/7, not just while at the doctor’s office. The wearable enables collection of both historical and point-in-time data, and the platform enables in-depth analysis of the data. Alerts presented on a smartwatch can provide customized encouragement for good behavior (such as walking or stair climbing) and positive lifestyle choices (such as getting enough sleep). Such uses are ripe for gamification; if the wearer walks a certain number of steps (customized for the individual) rewards are unlocked. People are more likely to embrace a wearable if it provides an element of fun and positive feedback. Data from large numbers of wearers can be anonymized and aggregated to perform epidemiological studies. Data can be segmented by geography, activity level, and demographics if wearers choose to opt in. A wearable paired with a GPS-enabled smartphone can transmit coordinates and pertinent data to first responders in case of an emergency and alert family members of the wearer’s status. A surgeon wearing smart glasses can monitor patient vital signs and other medical equipment in real time during an operation without turning away from the patient. Think Small, Think Big As these use cases indicate, a platform for wearables in healthcare needs to operate on a micro level, sending customized, personalized alerts, recommendations and actions to individuals based on their own data. But a platform should also enable macro-level analysis of vast quantities of data to spot trends and identify correlations within large populations. The ability to analyze data on a large scale not only holds promise for medical research, but it also improves the wearable’s value to the individual user: An intelligent platform with access to individual and aggregate data can, for example, tell the difference between an heart rate spike due to exercise – a good thing to be encouraged – and a cardiac episode requiring attention and intervention on a case-by-case basis, not just a pre-set threshold. One last bit of good news for healthcare providers who want to embrace wearables: Doctors are more trusted than any other group with consumers’ personal data. According to research by Timothy Morey, Theo Forbath and Allison Schoop and published in the May 2015 issue of Harvard Business Review, 87 percent of consumers find primary care doctors “trustworthy” or “completely trustworthy” with their personal data. That percentage is greater than credit card companies (85 percent), e-commerce firms (80 percent), and consumer electronics firms (77 percent), and much higher than social media firms (56 percent). As wearable use grows, that healthy goodwill is worth building on. Smartwatch image by Robert Scoble

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BIRT Data Objects: More Than Meets the Eye

If you think BIRT Data Objects (BDOs) are just for ad hoc queries, we have news for you: BDOs are much more than simply another semantic metadata layer. Unlike conventional metadata, which is used by many business intelligence and analysis tools to provide data access for non-technicians, BIRT Data Objects have capabilities that address many metadata shortcomings and enhance the performance of data-driven applications. What is Semantic Metadata, and How Does It Help? Data in most large companies lives in many systems, is diverse and complex, and requires highly skilled technical staff to acquire and make sense of it. To overcome this complexity, IT specialists create a semantic metadata layer to bridge the gap between the typical business user and the data. Semantic metadata abstracts things like SQL queries and business logic and transforms obscure database columns names to simple, easy-to-understand terms. Semantic metadata is an attempt to present data to the user in the friendliest possible way for autonomous report authoring. If effectively set up and maintained, a metadata layer can reduce or eliminate the need for IT intervention in the case of ad hoc queries and reports created by business users. With this promise, it’s easy to see why the metadata concept is ubiquitous in the current crop of business analysis tools. What’s Wrong with Semantic metadata? But a semantic metadata layer for reporting is not the holy grail of end-user empowerment that it purports to be. There are drawbacks to the concept, especially with those tools that use an Online Analytical Processing (OLAP) or “data cube” approach: The semantic metadata layer can be inflexible. When changes are made to the underlying data, remodeling and re-acquiring the source data is often required to inherit updated information in the database. Data cubes take forever to model, and can take just as long to generate, resulting in giant multi-purpose cubes that provide dubious performance for end users. Data cubes typically pre-aggregate data on defined hierarchies (also known as drill paths). Pre-aggregated hierarchies box in the user to the drill paths that the cube modeler has defined, stifling true data exploration. Users should be able to roll up or down the data in any path they choose. And the step of pre-aggregating adds loads of time and overhead to generating the cube, as each and every calculation must be performed and the result loaded to the cube in the proper sequence. Many tools require the semantic metadata layer before any reporting or visualizations can be created, which can actually limit productivity as users wait for modeling tasks to be completed in order to do simple things like see last quarter’s sales figures. Drawbacks aside, the concept of a simplified data access layer, and its benefits to the technical and business worker in an organization, are obvious, and in fact should be applied to applications as a whole, not just to empower the occasional ad hoc report. A Better Option: BIRT Data Objects BIRT, and specifically BIRT Data Objects (BDOs), help realize the promise of the semantic metadata layer by getting around these challenges and creating a data access mechanism strong enough to power an entire application.  BIRT Data Objects offer unique advantages to application developers, providing a combination of features for accessing, federating (joining) and delivering data from multiple data sources to millions of users efficiently. Prior to BIRT Data Objects, no single technology could meet the combination of requirements for real-time operational access to source data, data federation, and modeling of the data for delivery of analytic information into dashboards and visualizations. With BIRT Data Objects every requirement is met, and is all developed natively in the BIRT Designer environment. A unified data layer provides tremendous productivity benefits for developers who no longer need to map business requirements to separate technologies and efforts, such as disposable “just for the ad hoc user” applications. In addition to ad hoc report authoring in BIRT Report Studio, BDOs provide efficient easy access to data for dashboards, interactive reports and crosstabs, maps, custom visualizations, scorecards, spreadsheets, you name it. This ain’t your daddy’s semantic metadata layer! BDOs include: Performance optimizations for real-time direct data source access Push-down on joins, filters, sorts, etc. for faster and more efficient processing Query trimming on tables and columns. This is important if you join multiple databases, because you only connect to the database that has the data you need right now Re-synchronizing of semantic metadata to changes in the underlying database(s) Grouping, organizing, and custom ordering of fields Columnar backend, in-memory processing, enabling super-fast performance for analytic operations and calculations Dozens of direct connectors to data sources of all kinds Ability to cache for efficiency Are designed in Actuate BIRT Designer Pro, the same integrated environment where all other BIRT application components are built This last point is important; it’s the secret sauce to a BDO’s ability to power an entire application. BIRT developers assemble components in a built-in sandbox, leveraging BDOs for data access, and crafting the various aspects of the user experience. Built-in UX aspects include setting behavior like how dashboard charts drill to reports, how maps drill to dashboards, how certain data fields are visible to only certain users, and stuff like that. All of this is done in one designer: BIRT. A completed app is published as a whole to BIRT iHub, where it’s instantly available on the web to real users. The portability of an application, complete with data thanks to BDOs, enables sharing and collaboration. I know because my own team uses these tools to share and collaborate on demo BIRT apps, then efficiently deploy them to the field sales engineers with one click. Better Processing, Better Performance How do BDOs enable better processing of Big Data? The iHub BDO data architecture implements a dictionary that enables smaller representation of large-size values, greatly reducing the overall volume of the original data. Further, data is compressed using the Run Length Encoding (RLE) technique. The compression ratio achieved through RLE is heavily dependent on the degree to which data values are repeated, but generally results in an extremely high compression ratio. Additionally, BIRT iHub delivers significant performance gains through a number of techniques that maximize the amount of data  stored in memory. The data remains compressed in memory, and queries are executed directly on compressed data. Only the necessary columns are loaded for each query, and columns can be reused across queries. Also, query components (such as calculations) can be reused in different queries. The queries are executed directly on compressed data, further increasing the volume of data that can be retained in memory. The data is materialized much faster with BDOs than with more traditional semantic metadata cubes. Only the columns that are used are materialized, and the data representation is column based. Some (But Not All) BDO Benefits: Powers more than ad hoc queries. BDOs can be a cornerstone of an entire application deployed on the BIRT iHub Makes joining disparate data easy. You can include nosql, sql, csv, POJO, excel, etc, in the same BDO. Try doing that, easily at least, in SQL Nested BDOs can mix and match cached data with real time data as business need require Columnar, in-memory database has quick response times against massive amounts of data No need to pre-define and pre-aggregate cubes like an OLAP tool, resulting in faster analysis and drill in any direction Provides incremental updates. No need to refresh the entire data model just to capture new data Did I mention you can power entire applications with a BDO? It’s not just for ad hoc reporting! BDO-backed, data-driven applications can range from personalized portals to apps powering wearable devices like Nike Fuelband. Designing compelling data-driven experiences – especially for customer-facing apps that have both a large number of users and large data sets – remains an art and a science. Check out this free whitepaper on the “5 Best Practices for Designing Data-Driven Apps” and put your BDOs to work.   (Editor: All mentions of metadata have been clarified to read as “semantic” metadata)

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