Hit the road running with a new BI initiative

Boris Evelson

Even though Business Intelligence applications have been out there for decades lots of people still struggle with “how do I get started with BI”. I constantly deal with clients who mistakenly start their BI journey by selecting a BI platform or not thinking about the data architecture. I know it’s a HUGE oversimplification but in a nutshell here’s a simple roadmap (for a more complete roadmap please see the Roadmap document in Forrester BI Playbook) that will ensure that your BI strategy is aligned with your business strategy and you will hit the road running. The best way to start, IMHO, is from the performance management point of view:

  1. Catalog your organization business units and departments
  2. For each business unit /department ask questions about their business strategy and objectives
  3. Then ask about what goals do they set for themselves in order achieve the objectives
  4. Next ask what metrics and indicators do they use to track where they are against their goals and objectives. Good rule of thumb: no business area, department needs to track more than 20 to 30 metrics. More than that is unmanageable.
  5. Then ask questions how they would like to slice/dice these metrics (by time period, by region, by business unit, by customer segment, etc)
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Systems Of Insight: Next Generation Business Intelligence

Boris Evelson

Earlier Generation BI Needs A Tune Up

Business intelligence has gone through multiple iterations in the past few decades. While BI's evolution has addressed some of the technology and process shortcomings of the earlier management information systems, BI teams still face challenges. Enterprises are transforming only 40% of their structured data and 31% of their unstructured data into information and insights. In addition, 63% of organizations still use spreadsheet-based applications for more than half of their decisions. Many earlier and current enterprise BI deployments:

  • Have hit the limits of scalability.
  • Struggle to address rapid changes in customer and regulatory requirements.
  • Fail to break through waterfall's design limitations.
  • Suffer from mismatched business and technology priorities and languages.
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China Unicom Monetizes Customer Behavior Data

Gene Cao

China Unicom demonstrated its big data analytics platform, including customer analytics, during the Shanghai World Mobile Congress last week. Huawei is helping China Unicom’s Shanghai affiliate build a big data analytics platform that can collect and analyze customer demographics and operational and behavioral data. For instance, it can estimate a consumer’s monthly income based on annual mobile fees, know whether she is walking or driving, and what routes she regularly takes. Such data is unique and even more comprehensive than that generated by Internet service giants like Baidu, Alibaba, or Tencent. China Unicom will begin to leverage this data analytics platform to monetize data in several ways:


  • Retain customers. China Unicom can predict which high-value customers may be thinking of dropping its services and target marketing based on the customers’ context to retain them. For instance, the system can automatically send a targeted offering when a customer passes by a China Unicom office. In 2014, the telco performed A/B testing among 200,000 Unicom subscribers in Shanghai who were thinking of changing telecom service providers. This helped China Unicom retain RMB 10 million in revenue from those users receiving targeted marketing offerings from the system.
  • Enhance public security. China Unicom uses the platform to help the Shanghai city government to monitor people’s location in the city in real time. The system provides a real-time heat map and automatically sends an alert when it discovers that too many people are crowded into one area. This can help the government avoid accidents such as the one that occurred in the Waitan district of Shanghai last year.
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The Quantified Workforce Drives Improved Productivity And Safety

JP Gownder

You've probably heard about the Quantified Self (QS), a movement that aims to capture, analyze, and act upon data from the human body in the interest of better health, fitter athletes, and sharper minds. Today, QS is giving way to QW -- Quantified Workforce. A variety of technologies -- devices, software, services -- can quantify the health, fitness, mental acuity, timeliness, and collaboration of workers. Many of these services are ready for prime time, but present some challenges in implementing. These challenges aren't primarily technological; they're related to privacy, workers' rights, and human resources policies. Done right, though, quantifying the workforce can drive both top- and bottom- line growth in your company's business.

I've analyzed this trend in a new report, Smart Body, Smarter Workforce. Here are just a couple of examples of how quantifying the workforce can drive better business outcomes:

  • Lower the company's insurance rates. In January, 2014, Forrester predicted that insurance companies would offer lower rates to individuals who donned wearables -- and we are now seeing that response. In April, 2015, John Hancock announced an opportunity for buyers of its term and life insurance policies to earn up to 15% discount on their insurance rates by wearing a Fitbit, sharing the data with the company, and meeting certain activity levels.  
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Data Today Keeps The Doctor Away

Jennifer Belissent, Ph.D.

In scanning through my O’Reilly Data Newsletter today, I noticed A Healthy Dose of Data, an MIT Sloan case study on the data and analytics culture at Intermountain, a healthcare network that runs 22 hospitals and 185 clinics.  The study is definitely worth the read.  It reviews the history of data use at Intermountain, which began way before the “big data” craze of recent years.  In fact, it was back in the 1950s that one of the Intermountain cardiologists, Homer Warner, began to explore clinical data to understand why some heart patients experienced better outcomes than others.  He went on to become known as the “father of medical informatics – the use of computer programs to analyze patient data to determine treatment protocols,” and with colleagues designed and launched their first decision-support tool. 

The case study goes on to describe how Intermountain has cultivated a strong data and analytics culture. Over time – Rome was not built in a day, as they say – they established data maturity across the organization by investing in the capacity (new tools and technologies), developing the competencies (new skills and processes) and finally spreading the culture (awareness, understanding and best practices) of data and analytics. Their analytical approach brought results – fewer surgical infections, more effective use of antibiotics, less time in intensive care etc – contributing to lower costs, better medical outcomes, and overall patient satisfaction.

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The API Management Solutions Market Will Quadruple By 2020 As Business Goes Digital

Michael Yamnitsky
Often considered the poster child of digital transformation, APIs are proliferating at enterprises making industry-leading investments in mobile, IoT, and big data. As these initiatives mature, CIOs, CTOs, and heads of development are coming together with business leaders to manage and secure companywide use of APIs using API management solutions
Forrester recently released a report that sizes and projects annual spending on API management solutions. We predict US companies alone will spend nearly $3 billion on API management over the next five years. Annual spend will quadruple by the end of the decade, from $140 million in 2014 to $660 million in 2020. International sales will take the global market over the billion dollar mark.
In interviewing vendors for this piece of research, we discovered a vast and fertile landscape of participants:
Startups have taken $430 million in venture funding, and so far have realized $335 million in acquisition value. In April 2015, pure-play vendor Apigee went IPO and currently trades at a valuation north of $400 million. 
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Expand Your Big Data Capabilities With Unstructured Text Analytics

Boris Evelson
Beware of insights! Real danger lurks behind the promise of big data to bring more data to more people faster, better, and cheaper: Insights are only as good as how people interpret the information presented to them. When looking at a stock chart, you can't even answer the simplest question — "Is the latest stock price move good or bad for my portfolio?" — without understanding the context: where you are in your investment journey and whether you're looking to buy or sell. While structured data can provide some context — like checkboxes indicating your income range, investment experience, investment objectives, and risk tolerance levels — unstructured data sources contain several orders of magnitude more context. An email exchange with a financial advisor indicating your experience with a particular investment vehicle, news articles about the market segment heavily represented in your portfolio, and social media posts about companies in which you've invested or plan to invest can all generate much broader and deeper context to better inform your decision to buy or sell. 
But defining the context by finding structures, patterns, and meaning in unstructured data is not a simple process. As a result, firms face a gap between data and insights; while they are awash in an abundance of customer and marketing data, they struggle to convert this data into the insights needed to win, serve, and retain customers. In general, Forrester has found that: 
  • The problem is not a lack of data. Most companies have access to plenty of customer feedback surveys, contact center records, mobile tracking data, loyalty program activities, and social media feeds — but, alas, it's not easily available to business leaders to help them make decisions. 
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Become Customer-Obsessed Or Fail

Michael Gazala

What’s the top imperative at your company? If it’s not a transformation to make the company more customer-focused, you’re making a mistake. Technology and economic forces have changed the world so much that an obsession with winning, serving, and retaining customers is the only possible response.

We’re in an era of persistent economic imbalances defined by erratic economic growth, deflationary fears, an oversupply of labor, and surplus capital hunting returns in a sea of record-low interest rates. This abundance of capital and labor means that the path from good idea to customer-ready product has never been easier, and seamless access to all of the off-the-shelf components needed for a startup fuels the rise of weightless companies, which further intensify competition.

Chastened by a weak economy, presented with copious options, and empowered with technology, consumers have more market muscle than ever before. The information advantage tips to consumers with ratings and review sites. They claim pricing power by showrooming. And the only location that matters is the mobile phone in their hand from which they can buy anything from anyone and have it delivered anywhere.

This customer-driven change is remaking every industry. Cable and satellite operators lost almost 400,000 video subscribers in 2013 and 2014 as customers dropped them for the likes of Netflix. Lending Club, an alternative to commercial banks, has facilitated more than $6 billion in peer-to-peer loans. Now that most B2B buyers would rather buy from a website than a salesperson, we estimate that 1 million B2B sales jobs will disappear in the coming years.

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How Data Can Enable Business Disruption: Traditional Retailers Must Take Note Of The Sharing Economy

Dan Bieler

Recently, I talked with the CEO and founder of reBuy about the shifting dynamics in the retail sector as a result of digitalization. The use of data has evolved to the point where data has become the enterprise’s most critical business asset in the age of the customer. The business model of reBuy reCommerce — the leading German marketplace for secondhand goods — can help CIOs understand how the intelligent use of data can significantly disrupt a market such as retail.

The case of reBuy offers interesting insights into how the wider trends of the sharing and collaborative economy affect retail. If you can buy a good-quality used product with a guarantee for half the price, many people will not buy the product new. Many consumers increasingly accept product reuse and see it as an opportunity to obtain cheaper products and reduce their environmental footprint by avoiding the production of items that wouldn’t be used efficiently. The reBuy case study highlights that:

  • Business technology is taking the sharing economy into new realms. The reBuy business model demonstrates that consumers are starting to push the ideas of the sharing economy deep into the retail space. CIOs in all industries must prepare for the implications that this will have for their businesses.
  • Standalone products are at particular risk of sharing dynamics. The example of reBuy shows that businesses that sell plain products will come under even more pressure from shifting shopping behavior, where people are increasingly satisfied with buying used goods. These businesses need to add value to those products that are not available for secondhand purchase.
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Intel Announces Xeon SOC – Seriously Raising the Bar for AMD and ARM Competition

Richard Fichera

Intel has made no secret of its development of the Xeon D, an SOC product designed to take Xeon processing close to power levels and product niches currently occupied by its lower-power and lower performance Atom line, and where emerging competition from ARM is more viable.

The new Xeon D-1500 is clear evidence that Intel “gets it” as far as platforms for hyperscale computing and other throughput per Watt and density-sensitive workloads, both in the enterprise and in the cloud are concerned. The D1500 breaks new ground in several areas:

It is the first Xeon SOC, combining 4 or 8 Xeon cores with embedded I/O including SATA, PCIe and multiple 10 nd 1 Gb Ethernet ports.

(Source: Intel)

It is the first of Intel’s 14 nm server chips expected to be introduced this year. This expected process shrink will also deliver a further performance and performance per Watt across the entire line of entry through mid-range server parts this year.

Why is this significant?

With the D-1500, Intel effectively draws a very deep line in the sand for emerging ARM technology as well as for AMD. The D1500, with 20W – 45W power, delivers the lower end of Xeon performance at power and density levels previously associated with Atom, and close enough to what is expected from the newer generation of higher performance ARM chips to once again call into question the viability of ARM on a pure performance and efficiency basis. While ARM implementations with embedded accelerators such as DSPs may still be attractive in selected workloads, the availability of a mainstream x86 option at these power levels may blunt the pace of ARM design wins both for general-purpose servers as well as embedded designs, notably for storage systems.

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