Tag Archives: analytics

5/17 Webinar: A look at the Common Data Service for Apps, Common Data Service for Analytics and Power BI Insights Apps

On March 21st the Business Applications Group announced a couple of new technologies: Common Data Service for Apps, Common Data Service for Analytics and Power BI Insights Apps.

In this demo heavy webinar Microsoft program managers, Charles Sterling and Matthew Roche, will take a tour of the Common Data Service for Apps, Common Data Service for Analytics and Power BI Insights Apps.  Demos to include creating PowerApps Canvas based application that put data into the Common Data Service, a Model Based PowerApps application that is built on top of the Common Data Service, creating a Common Data Service Analytics Data Pool with online Power Query, creating reports with Power BI Desktop against a Common Data Service Analytics Data Pool and finally showing how to get instant value from Common Data Service Analytics using Power BI Insight Apps.

When:  5/17/2018 10AM PST

Where: https://www.youtube.com/watch?v=1lYcHgDllxE 

 5/17 Webinar: A look at the Common Data Service for Apps, Common Data Service for Analytics and Power BI Insights Apps

Presented by Mathew Roche and Charles Sterling

Matthew Roche is an experienced program manager, data architect, software developer, trainer and mentor with over two decades of experience in the Microsoft data platform and developer ecosystem. His current role as Senior Program Manager on the Microsoft Cloud & Enterprise team allows him to extend the features and influence the direction of Microsoft Business Intelligence, Data Governance, and Information Management products and services. 

Before joining Microsoft in 2008, Matthew was a Microsoft Most Valuable Professional (MVP) for SQL Server. Matthew holds a wide range of professional certifications including Microsoft Certified Trainer, Microsoft Certified Database Administrator, Microsoft Certified Solution Developer, Microsoft Certified Technology Specialist, Microsoft Certified Professional Developer, Microsoft Certified IT Professional and Oracle Certified Professional.

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Hot Topics at IDUG 2018: Data Analytics, Encryption and GDPR

From its humble beginnings back in 1988, the International DB2 Users Group has transformed to offer a range of content to its members, including educational events, technical courses, and yearly conferences – one of which took place in Philadelphia this year. The 2018 IDUG Db2 Tech Conference in  kicked off with a keynote speech by IDUG Founder Andrew Filipowski. He gave the audience a brief history of IDUG on this occasion of its 30th anniversary.

Hot Topics at IDUG 2018 Data Analytics Encryption and GDPR banner Hot Topics at IDUG 2018: Data Analytics, Encryption and GDPR

IDUG 2018 was a particularly vibrant meeting in a vibrant city. The setting for the conference was right in the middle of the megalopolis, the corridor of Boston to DC which made for an ideal location for attendees – many of whom could drive to the event instead of fly. It was an intimate conference with lots of networking, old friends and colleagues meeting and greeting, generous mind-sharing and experience sharing. Everyone was eager to hear what’s new, what’s better. All wanted to bring new perspectives back to their organizations.

While I was limited to attending the Solutions Center as an Exhibitor at the Syncsort booth I did get the chance to ask visitors what they found new and exciting at the conference.

Some were quite practical and said they were there to hear about user experiences during migrations to Db2 V 12.  Other were researching the concept of Db2 hybrid a combination of Db2 on premise and in the cloud. Our booth neighbor, Segus, had a steady stream of visitors inquiring about their Db2 compliance solutions. There was lots of buzz around compliance, particularly GDPR and encryption. Presentations were given to highlight best practices for GDPR compliance, as well as the latest techniques and security mechanisms used with Db2 to enhance a user’s database.

The Syncsort booth had a good turnout of visitors also inquiring about Ironstream, our z/OS forwarder of log data to Splunk and Elastic Stack, our solutions DL/2 and VS/2 for migrating IMS and VSAM data to DB2 and our legacy MFX and ZPSaver zIIP offload and sorting software.

Overall I can say this conference was about data – gaining access to data, processing data more quickly and efficiently, but mostly analyzing data for business insights. It was a great venue for Syncsort because our mantra is that we organize data everywhere to keep the world working.

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The Art and Science of Action-Driven Visual Analytics

Last year I went to our CMO Chris Capossela’s talk called “What’s Great Data in Microsoft”. In this talk, he listed five of the most important characteristics of good data: self-describe, fresh, forward thinking, inclusive, and adopted. The last one – adopted – is what he emphasized the most, and he challenged us to think harder about turning data into business actions.

How many business actions have you provoked with data? What methodology and thought process do you use to achieve these successes? Please contribute your ideas below in the commenting field.

In my experience, there is a lot of art and science behind every action-driven visual analysis. From defining business problems, analyzing data, designing powerful visualizations, constructing impactful narratives, this is the space where business meets data, art meets science, emotion meets technology, and creativity meets rationality. To convince our users to act on something, you must tackle them all.

Below is a graph I created based on Steven Few’s Tapping the Power of Visual Perception, John Medina’s Brain Rules, and a range of cognitive psychology articles I read. As humans, we face large amount of data and information every day. To derive meaning and make sense of this world, we constantly scan the world around us and selecting what is important and what is not. Iconic memory is our first layer of filter, and we will not pass on this information for further processing (short-term memory) unless we have an interest in the subject. Actions? It is even harder. How likely are we willing to act on anything if they don’t touch our hearts, bring us benefits, or prevent us from losing something (long-term memory)?

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In the world of big data, a visualization is merely a vehicle – a vehicle for us to create patterns, familiarity, and salience with data so that we can attract users’ attention and tap into their iconic memories, but to convince them to take actions, we must think deeper and tap into their short-term and long-term memories: Who is my audience? Why should they care? Will I make their jobs easier and help them create more impact?

With this framework in mind, let’s look at the two data visualization examples below and see which one is more effective? For illustration purpose, let’s assume that the user for this data visualization is a project manager at an IT Consulting Agency. Her performance is measured by the number of projects she does and how quickly she delivers solutions to her customers. To achieve high impact, she constantly looks for areas that hinder her effort or projects that drag down her performance.

Below are two different data visualizations that try to impact the same project manager. Please read and think about which one is better at serving her need and why? Will any of these visualizations provoke business actions and why?

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I think the thought process behind the 2nd visualization is more apparent than the 1st one. Considering the project manager is concerned about project speed, the 2nd visualization has added a “Above Median” calculation to separate concerning projects from the ones that are going well, and then effectively used visualization tools to surface those items that need attention:

  • Added a multi-pane dot chart to display all projects and surface those concerning projects by using salience (red stands out against blue). You can download MAQ software’s dot chart from the custom visual gallery.
  • Added “Variance from mean” to the text table, use data bars and color (same red/blue as the dot charts) to highlight projects that are above means.
  • Arranged the two bar charts together at the right, made them both vertical bars, and aligned their categories so that we can compare effective between the two charts, e.g. simple project counts and simple projects time are aligned vertically.
  • Added a title to top of the tab to illustrate the focus of this visual
  • Added a KPI filter to allow users filter out non-actionable items and focus on the actionable ones.

The magic of action-driven visual analysis is never about the beauty of the chart, but rather the thought process that goes behind it: identify what is important for your audience, and then use visualization tools to surface what they care about.

To learn more about this framework, check out my recent webinar recording for Power BI customers and prospects. Have great ideas on how to drive actions with data? Join the discussions by using the commenting field below.

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Why AI-Driven Analytics Will Have the Measure of the Digital Enterprise

iStock 645609968 e1525190912340 Why AI Driven Analytics Will Have the Measure of the Digital Enterprise

As the name succinctly says, Big Data has always been about scope, scale, and volume, a more the merrier ethos when it came to the intelligence suddenly at our disposal.

This sheer volume of insight was considered a prerequisite for accurate decision-making. It brought gigabytes and terabytes to mainstream parlance while signaling a whole new era of data handling and management with greater complexity and opportunity in terms of application.

However, as this data overload became the new norm, our expectations evolved to demand a more strategic approach to how we can derive greater value from the intelligence flooding our systems. Many have learned the hard way that more isn’t always better when it comes to data-driven decision making, as quantity rarely surpasses quality when it comes to more discerning and meaningful interventions.

Perhaps not surprisingly, this realization brings us to a growing trend in the digital enterprise. Defined by a shift in mindset from simply collecting intelligence to the more forensic measures used to drill down into it and extract the specifics, the use of advanced analytics is enabling us to do more with less data. This move increasingly underpins higher operating margins and ultimately creates a competitive advantage.

Fuelled by machine learning technology, this traction is in fact, forcing a re-evaluation of the traditional ‘more is more’ Big Data strategy that has long been a pervasive mantra in the digital environment. In this context, Artificial Intelligence, combined with visual analytics, becomes the game changer. This powerful proposition is perfectly equipped to sift through the reams of information and define the relationships and anomalies between the data sets to uncover actionable intelligence that augments human intelligence for greater business value.

While AI puts in the groundwork, at a speed and scale impossible for a human to compete with, visual analytics provides a more accessible and intuitive approach to data analysis. This can have deeper repercussions for an organization more broadly, specifically in terms of promoting a more entrenched data culture. As a result, a greater section of the workforce can be involved in data-driven decision making, as opposed to a privileged few, a move that far from simply being a ‘nice to do’ will become a fundamental requirement to offset the scarcity of specialist data skills. Figures from McKinsey and Company highlight the extent of the problem, with the US, for example, facing a shortage of 190,000 people with analytical data skills and 1.5 million managers and analysts equipped to understand and make decisions based on the analysis of Big Data.

So how do AI-powered analytical innovation and data democracy translate into tangible gains when applied to the real-world industry environment? Notable proponents such as the health, finance, and manufacturing sectors have been quick to embrace the approach. Healthcare professionals are harnessing the technology to detect abnormalities in X-rays, for example. And banks are using the advanced algorithmic capabilities to drive deep content analysis of a customer’s financial status, objectives, risk aversion and can respond to the nuances of their personality and behavior to best tailor the approach to the user’s needs.

Now we see others playing catch up, such as the oil and gas sector. Here, pressure over ever tighter margins induced by the global drop in oil price has demanded even greater accurate insight to optimize production, as well as to inform continuous monitoring and intervention needed for the smooth and seamless running of critical operations. As a result, a more measurable and quantifiable approach to the enterprise becomes the game changer, as science and business converge ever more closely to keep the business efficient and competitive.

Machine learning is harnessed to provide recommendations and to make predictions over the control and management of assets processing billions of data points in real-time from equipment ratings to thermal gradients. This creates a full picture and level of precision that removes all guesswork from the equation.  

As a result, gut feel is replaced with an augmented human brain, thanks to algorithmic prowess.

*As first appeared on Information Age.

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The TIBCO Blog

Analytics Just Got Friendlier

websitelogo Analytics Just Got Friendlier

Posted by Anand Misra, Principal Product Marketing Manager

NetSuite has long been a leader in embedded analytics. We enable our customers to drive actionable analysis, enforce business processes and gain meaningful insight into company performance.

NetSuite’s new SuiteAnalytics solution has a self-serve, personalized UI with easy-to-use search and reporting tools. Its customizable reporting capabilities look less like a spreadsheet and more like a friendly, familiar tool. It delivers information in real time at no added cost, eliminating the need for developers and IT to deal with separate reporting tools.

With the new SuiteAnalytics, non-technical users can get real-time insight without waiting for IT. The user interface is designed for business users with limited or no technical knowledge of database schemas. Query language can easily create complex queries, joins, pivot tables and charts through actions such as drag-and-drop editing. Other advancements for non-technical users include:

  • A brand-new, consumer-oriented user experience.
  • Improved ease: there’s drag-and-drop editing, filtering, and easy setup.
  • Rich visualizations, formatting, charting and layouts, delivering insights that are not only more accurate, but easier to understand across the organization.
  • Dynamic interaction with live data
  • Instant previews

The new SuiteAnalytics leverages a new unified metadata layer within NetSuite, allowing users to understand the business context of the data rather than traditional record views. For more technical users, the enhancements include:

  • Multi-level Business Entity joins
  • Ability to build queries on top of queries
  • Pivoting capabilities
  • A unified view of the entire solution
  • Significant performance improvements.

“I love the ability to pivot on data now,” said Rivka Lund, a beta user with Noon Home Inc. “This feature is amazing!!!“

According to Andreas Mantius of the Clover Group, “The power of NetSuite’s new SuiteAnalytics tool will allow us to evolve and advance our reports and metrics, providing us with more intelligence to drive key business decisions.” Existing NetSuite customers interested in signing up to beta test the latest enhancements to SuiteAnalytics should contact Beta.SuiteAnalytics@netsuite.com.

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How Tipping Point Is Seeking Financial Justice with TIBCO Analytics

In March 2018, the San Francisco Municipal Transportation Agency (SFMTA) announced reforms that would alleviate the burden of parking ticket fines on low-income San Franciscans. There is increasing awareness that parking citations can result in disproportionate hardship for low income drivers, from late fees, towing, and even loss of income resulting from the impounding or sale of a vehicle. Tipping Point Community, a non-profit that fights poverty in the Bay Area, has been working hard behind the scenes to promote greater awareness of this issue, sharing statistics and recommendations with the San Francisco city government. They’ve spent the last few months crunching through some pretty big datasets to quantify just how bad the problem is.

Ashley Brown, a manager and analyst on the Impact + Learning team at Tipping Point, used TIBCO’s Spotfire Data Science platform to uncover insights from parking citation data. She took advantage of the collaborative capabilities of the platform to work alongside some of TIBCO’s own data scientists. Together, they used the point-and-click analytics tools to develop an array of statistics and visualizations that highlighted the most burdensome aspects of parking citations. The team also used TIBCO Spotfire to create dashboards for communicating the findings to other members of the organization.

The project started last year when Tipping Point came to TIBCO asking for help with analyzing the large parking datasets that it had acquired from the San Francisco MTA through the SF Sunshine Ordinance and the California Public Records Act. The analysts at Tipping Point were most comfortable with tools like Excel and Stata, so they needed a solution that was equally easy to use, but that could easily handle and clan millions of rows of citations and then combine the citation data with neighborhood attributes, demographics, towing data, and more.

They also wanted to move quickly — San Francisco founded the country’s first Financial Justice Project in early 2017, a new venture in conjunction with San Francisco’s Office of the Treasurer and Tax Collector, and Tipping Point was eager to take advantage of the City’s interest in “assessing and reforming how fees and fines impact our most vulnerable residents.” TIBCO recommended deploying Spotfire Data Science within Amazon Web Services, where it can leverage scalable cloud-based platforms like EMR and Redshift. Within a few hours, the team was able to upload datasets and produce their first data workflows.

dashboard How Tipping Point Is Seeking Financial Justice with TIBCO Analytics

Initially, the data scientists from Tipping Point looked for patterns in the way citations were given in different neighborhoods. But what does it mean to compare one ZIP code to another? How should they take into account the size of the area and the density of parking meters or tow away zones? So they started looking along other dimensions — the type of ticket, the make of the vehicle. And immediately they saw that tickets were actually more expensive for certain vehicle types that were perhaps correlated with low or middle incomes. Even tickets of the same type (e.g. street sweeping) had varying costs, most likely because of late fees. The team wanted to measure just how great a burden came from more expensive tickets (e.g. lapsed registrations) and late fees and tow away fines, and if that burden might be higher for low-income people not just because they had less money, but because the tickets they got were actually — in effect — more expensive.

But there was no easy way to relate the income of a driver to the vehicle listed on the citation. The most reliable way to track a driver’s details was from the VIN number, but only a small proportion of tickets had that information. The analysts could use the make of the vehicle, but that was a very weak proxy for income. Eventually, they realized that California license plates held the key — they are issued sequentially (7ABC111, 7ABC112, etc.) and generally remain with the vehicle when it is sold. So the digits on the plate can be converted into a unitless measure of age. Meanwhile, intuition suggests that vehicle age is directly correlated with income, a hypothesis confirmed by several studies (e.g. UT Austin, U.S. Department of Transportation).

The team now had a simple way to measure the impact of ticket costs on owners of aging vehicles. And the results were quite striking: for older vehicles, citations cost 14% more in general, and certain ticket types (e.g. expired registration) cost 32% more; delinquency rates were more than twice as high as the average.

These findings confirm what we hear anecdotally, of people who cannot pay tickets, who rapidly accumulate fees that can be fully half of the original fine, and who then lose their cars and the ability to get to work. Quantifying this — which tickets are most burdensome, and by how much — is the first step to making the system more fair. So TIBCO is proud to support the analysts of Tipping Point and the City employees working to change the way citations are managed.

workspace How Tipping Point Is Seeking Financial Justice with TIBCO Analytics

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The Challenge Of Analytics Growth In The Public Sector

For nerds, the weeks right before finals are a Cinderella moment. Suddenly they’re stars. Pocket protectors are fashionable; people find their jokes a whole lot funnier; Dungeons & Dragons sounds cool.

Many CIOs are enjoying this kind of moment now, as companies everywhere face the business equivalent of a final exam for a vital class they have managed to mostly avoid so far: digital transformation.

But as always, there is a limit to nerdy magic. No matter how helpful CIOs try to be, their classmates still won’t pass if they don’t learn the material. With IT increasingly central to every business—from the customer experience to the offering to the business model itself—we all need to start thinking like CIOs.

Pass the digital transformation exam, and you probably have a bright future ahead. A recent SAP-Oxford Economics study of 3,100 organizations in a variety of industries across 17 countries found that the companies that have taken the lead in digital transformation earn higher profits and revenues and have more competitive differentiation than their peers. They also expect 23% more revenue growth from their digital initiatives over the next two years—an estimate 2.5 to 4 times larger than the average company’s.

But the market is grading on a steep curve: this same SAP-Oxford study found that only 3% have completed some degree of digital transformation across their organization. Other surveys also suggest that most companies won’t be graduating anytime soon: in one recent survey of 450 heads of digital transformation for enterprises in the United States, United Kingdom, France, and Germany by technology company Couchbase, 90% agreed that most digital projects fail to meet expectations and deliver only incremental improvements. Worse: over half (54%) believe that organizations that don’t succeed with their transformation project will fail or be absorbed by a savvier competitor within four years.

Companies that are making the grade understand that unlike earlier technical advances, digital transformation doesn’t just support the business, it’s the future of the business. That’s why 60% of digital leading companies have entrusted the leadership of their transformation to their CIO, and that’s why experts say businesspeople must do more than have a vague understanding of the technology. They must also master a way of thinking and looking at business challenges that is unfamiliar to most people outside the IT department.

In other words, if you don’t think like a CIO yet, now is a very good time to learn.

However, given that you probably don’t have a spare 15 years to learn what your CIO knows, we asked the experts what makes CIO thinking distinctive. Here are the top eight mind hacks.

1. Think in Systems

Q118 Feature3 img1 Jump The Challenge Of Analytics Growth In The Public SectorA lot of businesspeople are used to seeing their organization as a series of loosely joined silos. But in the world of digital business, everything is part of a larger system.

CIOs have known for a long time that smart processes win. Whether they were installing enterprise resource planning systems or working with the business to imagine the customer’s journey, they always had to think in holistic ways that crossed traditional departmental, functional, and operational boundaries.

Unlike other business leaders, CIOs spend their careers looking across systems. Why did our supply chain go down? How can we support this new business initiative beyond a single department or function? Now supported by end-to-end process methodologies such as design thinking, good CIOs have developed a way of looking at the company that can lead to radical simplifications that can reduce cost and improve performance at the same time.

They are also used to thinking beyond temporal boundaries. “This idea that the power of technology doubles every two years means that as you’re planning ahead you can’t think in terms of a linear process, you have to think in terms of huge jumps,” says Jay Ferro, CIO of TransPerfect, a New York–based global translation firm.

No wonder the SAP-Oxford transformation study found that one of the values transformational leaders shared was a tendency to look beyond silos and view the digital transformation as a company-wide initiative.

This will come in handy because in digital transformation, not only do business processes evolve but the company’s entire value proposition changes, says Jeanne Ross, principal research scientist at the Center for Information Systems Research at the Massachusetts Institute of Technology (MIT). “It either already has or it’s going to, because digital technologies make things possible that weren’t possible before,” she explains.

2. Work in Diverse Teams

When it comes to large projects, CIOs have always needed input from a diverse collection of businesspeople to be successful. The best have developed ways to convince and cajole reluctant participants to come to the table. They seek out technology enthusiasts in the business and those who are respected by their peers to help build passion and commitment among the halfhearted.

Digital transformation amps up the urgency for building diverse teams even further. “A small, focused group simply won’t have the same breadth of perspective as a team that includes a salesperson and a service person and a development person, as well as an IT person,” says Ross.

At Lenovo, the global technology giant, many of these cross-functional teams become so used to working together that it’s hard to tell where each member originally belonged: “You can’t tell who is business or IT; you can’t tell who is product, IT, or design,” says the company’s CIO, Arthur Hu.

One interesting corollary of this trend toward broader teamwork is that talent is a priority among digital leaders: they spend more on training their employees and partners than ordinary companies, as well as on hiring the people they need, according to the SAP-Oxford Economics survey. They’re also already being rewarded for their faith in their teams: 71% of leaders say that their successful digital transformation has made it easier for them to attract and retain talent, and 64% say that their employees are now more engaged than they were before the transformation.

3. Become a Consultant

Good CIOs have long needed to be internal consultants to the business. Ever since technology moved out of the glasshouse and onto employees’ desks, CIOs have not only needed a deep understanding of the goals of a given project but also to make sure that the project didn’t stray from those goals, even after the businesspeople who had ordered the project went back to their day jobs. “Businesspeople didn’t really need to get into the details of what IT was really doing,” recalls Ferro. “They just had a set of demands and said, ‘Hey, IT, go do that.’”

But that was then. Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants. “If you’re building a house, you don’t just disappear for six months and come back and go, ‘Oh, it looks pretty good,’” says Ferro. “You’re on that work site constantly and all of a sudden you’re looking at something, going, ‘Well, that looked really good on the blueprint, not sure it makes sense in reality. Let’s move that over six feet.’ Or, ‘I don’t know if I like that anymore.’ It’s really not much different in application development or for IT or technical projects, where on paper it looked really good and three weeks in, in that second sprint, you’re going, ‘Oh, now that I look at it, that’s really stupid.’”

4. Learn Horizontal Leadership

CIOs have always needed the ability to educate and influence other leaders that they don’t directly control. For major IT projects to be successful, they need other leaders to contribute budget, time, and resources from multiple areas of the business.

It’s a kind of horizontal leadership that will become critical for businesspeople to acquire in digital transformation. “The leadership role becomes one much more of coaching others across the organization—encouraging people to be creative, making sure everybody knows how to use data well,” Ross says.

In this team-based environment, having all the answers becomes less important. “It used to be that the best business executives and leaders had the best answers. Today that is no longer the case,” observes Gary Cokins, a technology consultant who focuses on analytics-based performance management. “Increasingly, it’s the executives and leaders who ask the best questions. There is too much volatility and uncertainty for them to rely on their intuition or past experiences.”

Many experts expect this trend to continue as the confluence of automation and data keeps chipping away at the organizational pyramid. “Hierarchical, command-and-control leadership will become obsolete,” says Edward Hess, professor of business administration and Batten executive-in-residence at the Darden School of Business at the University of Virginia. “Flatter, distributive leadership via teams will become the dominant structure.”

Q118 Feature3 img3 rock The Challenge Of Analytics Growth In The Public Sector5. Understand Process Design

When business processes were simpler, IT could analyze the process and improve it without input from the business. But today many processes are triggered on the fly by the customer, making a seamless customer experience more difficult to build without the benefit of a larger, multifunctional team. In a highly digitalized organization like Amazon, which releases thousands of new software programs each year, IT can no longer do it all.

While businesspeople aren’t expected to start coding, their involvement in process design is crucial. One of the techniques that many organizations have adopted to help IT and businesspeople visualize business processes together is design thinking (for more on design thinking techniques, see “A Cult of Creation“).

Customers aren’t the only ones who benefit from better processes. Among the 100 companies the SAP-Oxford Economics researchers have identified as digital leaders, two-thirds say that they are making their employees’ lives easier by eliminating process roadblocks that interfere with their ability to do their jobs. Ninety percent of leaders surveyed expect to see value from these projects in the next two years alone.

6. Learn to Keep Learning

The ability to learn and keep learning has been a part of IT from the start. Since the first mainframes in the 1950s, technologists have understood that they need to keep reinventing themselves and their skills to adapt to the changes around them.

Now that’s starting to become part of other job descriptions too. Many companies are investing in teaching their employees new digital skills. One South American auto products company, for example, has created a custom-education institute that trained 20,000 employees and partner-employees in 2016. In addition to training current staff, many leading digital companies are also hiring new employees and creating new roles, such as a chief robotics officer, to support their digital transformation efforts.

Nicolas van Zeebroeck, professor of information systems and digital business innovation at the Solvay Brussels School of Economics and Management at the Free University of Brussels, says that he expects the ability to learn quickly will remain crucial. “If I had to think of one critical skill,” he explains, “I would have to say it’s the ability to learn and keep learning—the ability to challenge the status quo and question what you take for granted.”

7. Fail Smarter

Traditionally, CIOs tended to be good at thinking through tests that would allow the company to experiment with new technology without risking the entire network.

This is another unfamiliar skill that smart managers are trying to pick up. “There’s a lot of trial and error in the best companies right now,” notes MIT’s Ross. But there’s a catch, she adds. “Most companies aren’t designed for trial and error—they’re trying to avoid an error,” she says.

Q118 Feature3 img4 fail The Challenge Of Analytics Growth In The Public SectorTo learn how to do it better, take your lead from IT, where many people have already learned to work in small, innovative teams that use agile development principles, advises Ross.

For example, business managers must learn how to think in terms of a minimum viable product: build a simple version of what you have in mind, test it, and if it works start building. You don’t build the whole thing at once anymore.… It’s really important to build things incrementally,” Ross says.

Flexibility and the ability to capitalize on accidental discoveries during experimentation are more important than having a concrete project plan, says Ross. At Spotify, the music service, and CarMax, the used-car retailer, change is driven not from the center but from small teams that have developed something new. “The thing you have to get comfortable with is not having the formalized plan that we would have traditionally relied on, because as soon as you insist on that, you limit your ability to keep learning,” Ross warns.

8. Understand the True Cost—and Speed—of Data

Gut instincts have never had much to do with being a CIO; now they should have less to do with being an ordinary manager as well, as data becomes more important.

As part of that calculation, businesspeople must have the ability to analyze the value of the data that they seek. “You’ll need to apply a pinch of knowledge salt to your data,” advises Solvay’s van Zeebroeck. “What really matters is the ability not just to tap into data but to see what is behind the data. Is it a fair representation? Is it impartial?”

Increasingly, businesspeople will need to do their analysis in real time, just as CIOs have always had to manage live systems and processes. Moving toward real-time reports and away from paper-based decisions increases accuracy and effectiveness—and leaves less time for long meetings and PowerPoint presentations (let us all rejoice).

Not Every CIO Is Ready

Of course, not all CIOs are ready for these changes. Just as high school has a lot of false positives—genius nerds who turn out to be merely nearsighted—so there are many CIOs who aren’t good role models for transformation.

Success as a CIO these days requires more than delivering near-perfect uptime, says Lenovo’s Hu. You need to be able to understand the business as well. Some CIOs simply don’t have all the business skills that are needed to succeed in the transformation. Others lack the internal clout: a 2016 KPMG study found that only 34% of CIOs report directly to the CEO.

This lack of a strategic perspective is holding back digital transformation at many organizations. They approach digital transformation as a cool, one-off project: we’re going to put this new mobile app in place and we’re done. But that’s not a systematic approach; it’s an island of innovation that doesn’t join up with the other islands of innovation. In the longer term, this kind of development creates more problems than it fixes.

Such organizations are not building in the capacity for change; they’re trying to get away with just doing it once rather than thinking about how they’re going to use digitalization as a means to constantly experiment and become a better company over the long term.

Q118 Feature3 img6 CIOready The Challenge Of Analytics Growth In The Public SectorAs a result, in some companies, the most interesting tech developments are happening despite IT, not because of it. “There’s an alarming digital divide within many companies. Marketers are developing nimble software to give customers an engaging, personalized experience, while IT departments remain focused on the legacy infrastructure. The front and back ends aren’t working together, resulting in appealing web sites and apps that don’t quite deliver,” writes George Colony, founder, chairman, and CEO of Forrester Research, in the MIT Sloan Management Review.

Thanks to cloud computing and easier development tools, many departments are developing on their own, without IT’s support. These days, anybody with a credit card can do it.

Traditionally, IT departments looked askance at these kinds of do-it-yourself shadow IT programs, but that’s changing. Ferro, for one, says that it’s better to look at those teams not as rogue groups but as people who are trying to help. “It’s less about ‘Hey, something’s escaped,’ and more about ‘No, we just actually grew our capacity and grew our ability to innovate,’” he explains.

“I don’t like the term ‘shadow IT,’” agrees Lenovo’s Hu. “I think it’s an artifact of a very traditional CIO team. If you think of it as shadow IT, you’re out of step with reality,” he says.

The reality today is that a company needs both a strong IT department and strong digital capacities outside its IT department. If the relationship is good, the CIO and IT become valuable allies in helping businesspeople add digital capabilities without disrupting or duplicating existing IT infrastructure.

If a company already has strong digital capacities, it should be able to move forward quickly, according to Ross. But many companies are still playing catch-up and aren’t even ready to begin transforming, as the SAP-Oxford Economics survey shows.

For enterprises where business and IT are unable to get their collective act together, Ross predicts that the next few years will be rough. “I think these companies ought to panic,” she says. D!


About the Authors

Thomas Saueressig is Chief Information Officer at SAP.

Timo Elliott is an Innovation Evangelist at SAP.

Sam Yen is Chief Design Officer at SAP and Managing Director of SAP Labs.

Bennett Voyles is a Berlin-based business writer.

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Digitalist Magazine

Demandbase Debuts ABM Analytics for Optimal Marketing Performance

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Demandbase officially announced ABM Analytics during its annual ABM Innovation Summit. The new tech, part of the company’s account-based marketing platform, leverages its account identification technology to help marketers analyze performance from advertising to pipeline and revenue figures. “AI gives B2B marketers a clear view into the behavior of their target accounts,” said CMO Peter Isaacson.
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New All-Day Session: Designing Modern Data and Analytics Solutions in Azure

 New All Day Session: Designing Modern Data and Analytics Solutions in Azure

I have a natural inclination to share information that I have learned. Being a hands-on techie is something I absolutely love, but I have a bit of educator in my blood as well. And, continually learning new skills is at the core of what makes me happy. All of which means that I aim to teach others in a way that I would want to learn. 

What Will You Learn? 

This session will very much be about planning the architecture and factors around decision-making, presented in a very practical and realistic way (full abstract can be found here). We will build the components for one reference architecture, using scripts that we will provide you. 

PreconTime New All Day Session: Designing Modern Data and Analytics Solutions in Azure

The full abstract can be found on the PASS Summit site. To highlight just a few of the topics that you’ll hear about:

  • Going to the cloud – What’s easier? What’s harder? What trade-offs can you expect to make with respect to cost, control, complexity, performance, and security?
  • Cloud design patterns – In what ways are cloud design patterns different from traditional on-premises solutions? How does that change the typical roles for developers and administrators?
  • Schema-on-read – In what scenarios does schema-on-read work extremely well? In which situations is it not ideal?
  • Patterns-based development – What automation techniques can save you time, improve efficiency, and reduce the chance for error? 
  • Architecture – What does a BI/analytics/DW architecture look like if we value the concept of polyglot persistence vs. architectural simplicity? What kind of differences should we be aware of if we are using a distributed architecture? What are the Azure options for supporting data science and self-service BI?
  • Data storage - When do we want to analyze data in place vs. move it to another data store? What technology options do we have in Azure, and what factors do we want to consider for deciding between data virtualization and data integration? In what cases can you take advantage of a data lake in your architecture? 

Who is the Target Audience?

The ideal audience member has some experience as a data engineer, BI professional, or database developer, and is in the early stages of migrating or building solutions in Azure.

This session is broad because the data platform offerings in Azure are broad with many choices and considerations. Our day job *is* planning and building data solutions in Azure. Meagan and I are very excited to help you get started with building a solid data architecture in Azure.

More details and to register: Designing Modern Data and Analytics Solutions in Azure

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Blog – SQL Chick

4 Revenue Benefits of Embedded Analytics for Application Vendors

While offering clear advantages to end-users, benefits of embedded analytics can also deliver scalability and revenue to the companies providing analytics to their customers.

Often overlooked, these benefits can make a big impact on your bottom line, positively influencing customer decision-making, encouraging new business, and even taking advantage of previously untapped monetization opportunities.

Let’s examine 4 main advantages of embedding an analytics solution in your B2B application.

EmbeedScale 770X250 770x250 4 Revenue Benefits of Embedded Analytics for Application Vendors

1. Increased Win Rate

Analytics has become a compulsory functionality in today’s B2B market. This means providing an analytics solution as part of your product or service that lacks one can immediately increase customer satisfaction, market positioning and adoption.

In addition, upgrading an application’s analytics solution also presents revenue opportunities. It’s an ideal way to keep current customers’ attention by offering new capabilities from an existing offering. It can also pique the interest of net-new customers, potentially securing more business overall.

2. Decreased Churn Rate

Customers will switch solutions when they aren’t getting the functionality they need. Because data analytics is associated with a competitive advantage, today, many decisions to switch solutions are driven by the need for more information and better analytics. By adding or upgrading your offering’s Analytics and BI, existing customers benefit from new capabilities that keep them from seeking a different solution.

On top of this, showing your clients that you’re constantly working to improve your product is impressive. You can ensure loyalty longevity from your current customer base by offering them not just new features, but functional ones that will make their lives easier.

3. Expanded Product Licensing

Embedding analytics doesn’t have to be limited to a single use case or product. Similar to the above point, adding or upgrading analytics functionality can grow your application, product or service’s potential user base. The more departments, teams, or business units that can utilize and realize value from your application, the bigger increase you’ll see in user or product licenses from new and existing customers.

4. Feature Monetization

Giving users the ability to customize your application with additional “pay to play” modules (outside of the main offering) can be an excellent way to maximize the flexibility and value of your application, product, or service. Offering an analytics module can be a lucrative addition to your customization portfolio due to the sharp market demand for analytics tools. The additional information supplied by the added analytics can be a line item for additional revenue.

Ready to See The Benefits of Embedding Analytics Into Your Offering?

The revenue opportunities analytics and BI present are real. But what does this mean for your company? How can you be sure that you’re choosing the analytics solution that can expand and grow as you do? Choosing the right analytics solution that dovetails seamlessly with your application, product or service is, of course, an important and strategic decision.

EmbeedScale 770X250 770x250 4 Revenue Benefits of Embedded Analytics for Application Vendors

Categories: BI Best Practices

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Blog – Sisense