Leading-edge executives at organizations drive growth, innovate, and disrupt industries through emerging technologies: social, mobile, cloud, analytics, sensors, GIS and others. 85% of executives in a recent survey shared that “the need to drive innovation and growth” would have a moderate or high impact on IT services spending. But, today’s technology buyers face a fragmented, fast-moving landscape of niche technology and services providers in newer spaces (social, mobile, cloud) as well as new offerings from their largest global partners.
Often the leading- and bleeding-edge disruption comes from business stakeholders, rather than IT or sourcing executives; sourcing executives struggle to keep up with the fast pace of change that business demands. Our research shows that this fragmented, divisional, silo approach to buying (often under the radar screen) can create risk and go against enterprise IT strategy decisions.
To help their organizations navigate through these emerging options, we have identified three key principles of IT sourcing strategy:
Change the rules for working with vendors and partners. To thrive in the world of digital disruption and to enable sourcing of emerging technologies and services that drive digital disruption, sourcing strategists must create new rules for working with technology partners. They must increase the emphasis on innovation and differentiation and treat partners who excel in these dimensions differently from other tiered suppliers.
The analytics community is experiencing a rebirth. A renewal. A renaissance. Why? Data is bursting from every corner, from every device, allowing brands to deliver relevant messages and offers to its customers. So, being an analytics connoisseur is important now more than ever. I mean, who else is going to play with all this data . . . and actually enjoy it?
Organizations must develop relevant marketing strategies across devices -- to different customers -- and have the advanced measurement and analytic frameworks to fuel decisions. And the perpetually connected customer is forcing organizations to act quickly, so near-real-time insights are paramount. My past research addresses this, specifically, how analytics professionals can use attribution as a way to understand the true value of each interaction point. This is even more complex because of the increase in cross-device usage. As a result,analytic pros are using savvy ways to connect information and to measure cross-device impact and incremental value.
Mobile BI and cloud BI are among the top trends that we track in the industry. Our upcoming Enterprise BI Platforms Wave™ will dedicate a significant portion of vendor evaluation on these two capabilities. These capabilities are far from yes/no checkmarks. Just asking vague questions like “Can you deliver your BI functionality on mobile devices?” and “Is your BI platform available in the cloud as software-as-a-service?” will lead to incomplete vendor answers, which in turn may lead you to make the wrong vendor selections. Instead, we plan to evaluate these two critical BI platform capabilities along the following parameters:
Animations. Does the product support animations? For example, if a particular dimension, such as time, has hundreds or thousands of values (as in daily values over multiple years), manually clicking through every day is not practical. Launching an automated, animated scroll up and down such a dimension is a more practical approach.
Reflections from the 10th Safer Internet Day Conference in Berlin, February 5th 2013
Earlier this month, I had the pleasure of speaking at the Safer Internet Day Conference in Berlin, organized by the Federal Ministry of Consumer Protection, Food and Agriculture and BITKOM, the German Association for Information Technology, Telecommunication and New Media. The conference title, ‘Big Data – Gold Mine or Dynamite?’ set the scene; after my little introductory speech on what big data really means and why this is a relevant topic for all of us (industry, consumers, and government), the follow-up presentations pretty much focused either on the ‘gold mine’ or the ‘dynamite’ aspect. To come straight to the point: I was very surprised, if not slightly shocked at how deep a gap became visible between the industry on the one side and the government (mainly the data protection authorities) on the other side.
While industry representatives, spearheaded by the BITKOM president Prof. Dieter Kempf and speakers from IBM, IMS Health, SAS, and others, highlighted interesting showcases and future opportunities for big data, Peter Schaar, the Federal Commissioner for Data Protection, seemed to be on a crusade to protect ‘innocent citizens’ from the ‘baddies’ in the industry.
BI is used to build, report, and analyze business performance metrics and indicators. What about measuring the performance of BI itself? How do you know if you have a high-performing, widely used BI environment? Is your opinion based on qualitative “pulse checks” or is it based on quantitative metrics? BI practitioners who preach to their business counterparts to run their business by the numbers need to eat their own dog food: run their BI environment, platforms, and apps by the numbers. For example, do you know:
How many reports and queries do end users create by themselves versus how many IT creates? That's a great efficiency metric.
How many clicks within a dashboard does it take to find an answer to a question? That’/s another great efficiency metric.
How long does each user stay within each report? Do they just run and print the reports, or export the data to Excel, or do they really slice, dice, and analyze the information? That’s a good example of how effective your BI environment is.
Do you see any patterns in BI usage? User by user, department by department, or line of business by line of business?
How many reports, queries, and other objects are being used, how many are shelfware (not being used)? How often are people using the ones that are being used?
Data management is becoming critical as organizations seek to better understand and target their customers, drive out inefficiency, and satisfy government regulations. Despite this, the maturity of data management practices at companies in China is generally poor.
I had an enlightening conversation with my colleague, senior analyst Michele Goetz, who covers all aspects of data management. She told me that in North America and Europe, data management maturity varies widely from company to company; only about 5% have mature practices and a robust data management infrastructure. Most organizations are still struggling to be agile and lack measurement, even if they already have data management platforms in place. Very few of them align adequately with their specific business or information strategy and organizational structure.
If we look at data management maturity in China, I suspect the results are even worse: that fewer than 1% of the companies are mature in terms of integrated strategy, agile execution and continuous performance measurement. Specifically:
The practice of data management is still in the early stages. Data management is not only about simply deploying technology like data warehousing or related middleware, but also means putting in place the strategy and architectural practice, including contextual services and metadata pattern modeling, to align with business focus. The current focus of Chinese enterprises for data management is mostly around data warehousing, master data management, and basic support for both end-to-end business processes and composite applications for top management decision-making. It’s still far from leveraging the valuable data in business processes and business analytics.
The 2013 New Year has begun with the removal from the global tech market outlook of one risk, that of the US economy going over the fiscal cliff. On New Year's day, the US House of Representatives followed the lead of the US Senate and passed a bill that extends existing tax rates for households with $450,000 or less in income, extends unemployment insurance benefits for 2 million Americans, and renews tax credits for child care, college tuition, and renewable energy production, as well as delaying for two months the automatic spending cuts. While it also allowed Social Security payroll taxes to rise by 2 percentage points — thereby raising the tax burden on poor and middle class people — and did not increase the federal debt ceiling or address entitlement spending, the last-minute compromise does mean that the US tech market no longer has to worry, for now, about big increases in taxes and cuts in spending pushing the US economy into recession.
While Social Business continued to evolve in 2012, 2013 will see the emergence of digital business as a new strategic theme for many firms. What's driving this shift and what does it mean for CIOs, CEOs, and chief digital officers?
The Communications Evolution
Communications continue to evolve. Consider how humans have transformed communications over the centuries: signal fires; semaphore; Morse code; the telegraph; the telephone; telex; fax; email; SMS; Facebook; and Twitter. I have no doubt that this evolution will continue in 2013 and beyond. Perhaps beyond 2013 we will eventually achieve the ability to communicate our thoughts directly — whether we’ll want to is a different question. As people the world over learn to use new social networking tools, they drop older tools that are no longer useful to them. Regardless of where you are in your personal communications evolution, the undeniable truth is that over the past decade we have significantly changed how people communicate; we are no longer dependent upon email. But social tools and 24/7 mobile access have not removed the complexity or decreased the volume of information we must process. Time remains our most precious resource and we’ll always seek ways to use it more effectively — but social tools are not necessarily the silver bullet we might think. In 2013 we need to rethink business processes to take this new communications paradigm into account.
Rowan Curran, Research Associate and TechnoPolitics producer, hosts this episode to ask me (your regular host) about The Pragmatic Definition Of Big Data. Listen (5 mins) to hear the genesis of this new definition of big data and why it is pragmatic and actionable for both business and IT professionals.
Podcast: The Pragmatic Definition Of Big Data Explained (5 mins)