When it comes to data technology, are you lost in translation? What's the difference between data federation, virtualization, and data or information-as-a-service? Are columnar databases also relational? Does one use the same or different tools for BAM (Business Activity Monitoring) and for CEP (Complex Event Processing)? These questions are just the tip of the iceberg of a plethora of terms and definitions in the rich and complex world of enterprise data and information. Enterprise application developers, data, and information architects manage multiple challenges on a daily basis already, and the last thing they need to deal with are misunderstandings of the various data technology component definitions.
Business decision-makers in Asia Pacific (AP) are increasingly aware of the importance of business intelligence (BI) and broader analytics to business strategy and execution. However, lack of internal expertise remains a significant barrier to BI project success.
To succeed in the region, BI service providers must provide guidance on how to translate data access into actual insight and information into business value. This requires a strong understanding of local cultures, business practices, regulatory frameworks, and market dynamics. When evaluating providers, understand how their capabilities are likely to evolve across five categories:
People. To minimize project risks, understand who will be the on-site business and technical leads on BI projects and how many successful implementations this staff has led in a similar industry and similar technical environment within the region.
Technical expertise. Service providers need to demonstrate region-specific knowledge of the technical characteristics of various BI tools, platforms, architectures, and applications. Most companies will not have all of the necessary skills on site, so closely evaluate ease of access to remote staff from the service provider as well.
When I posted a blog on Don’t Establish Data Management Standards (it was also on Information Management's website as Data Management Standards are a Barrier) I expected some resistance. I mean, why post a blog and not have the courage to be provocative, right? However, I have to say I was surprised at the level of resistance. Although, I also have to point out that this blog was also one of the most syndicated and recommended I have had. I will assume that there is a bit of an agreement with it as well as I didn't see any qualifiers in tweets that I was completely crazy. Anyway, here are just a few dissenter comments:
“This article would be funny if it wasn't so sad...you can't do *anything* in IT (especially innovate) without standing on the shoulders of some standard.” – John O
“Show me data management without standards and good process to review and update them and I'll show you the mortgage crisis which developed during 2007.” – Jim F
“This article is alarmingly naive, detrimental, and counterproductive. Let me count the ways…” – Cynthia H
"No control leads to caos... I would be amused to watch the reaction of the ISO engineer while reading this article :)." - Eduardo G (I would too!)
After wiping the rotten tomatoes from my face from that, here are some points made that get to the nuance I was hoping to create a discussion on:
It seems to be popular these days amongst industry pundits to recommend that organizations add a new Cxx role: the Chief Data Officer (CDO). The arguments in favor of this move are exactly what you'd think: the rapidly accelerating importance of information in the enterprise, and, as important, the heightened perception of the importance of information by business executives. The attention on information comes from all the rich new data that simply didn't exist before: sensor data from the Internet Of Things, social media, process data -- really just the enormous volume of data resulting from the digitization of everything. Add to all that: new technology to handle big data in a reasonable time frame, user-friendly mobile computing in the form of tablets, data virtualization software and data warehouse appliances that significantly accelerate the process of getting at the information for analysis, and the promise of predictive analytics, and there's plenty of cause for an information management rennaisance out there. With a little luck, the activity it catalyzes will also improve enterprises' ability to manage the data and content that's not so new but also very important that we've been struggling with for the last decade or so.
The only argument against creating this role that I've run across is that if CIOs and CTOs did their jobs right, we wouldn't need this new role. That's pretty feeble since we're not just talking about IT's history of relative ineffectiveness in managing information outside of application silos (and don't get me started about content management) -- we're adding to that a significant increase in the value of information and a significant increase in the amount of available information. And then there's the fact that the data could be in the cloud and not managed by IT, and there's also a changing picture regarding risk that suggests a new approach.
In a month or so I’ll be launching a survey to research issues around information strategy, information architecture and information management in general. I thought it might be useful to do a bit of crowdsourcing to get the best ideas for what questions to ask and make sure I’m covering your top-of-mind issues. We ask you all fairly often to provide answers to survey questions – maybe you’d like to provide input into the questions this time out?
Surveys are interesting – one is tempted to ask about everything imaginable to get good research data. But long onerous surveys produce very low percentages of completes vs. starts -- it’s classic case of less is more. Twenty completes for a very comprehensive survey is nowhere near as valuable as a couple hundred completes of a more limited survey. For example, I really wanted to provide an exhaustive list of tasks related to information management or information architecture practices and then provide an equally exhaustive list of organizational roles to get data on who does what in the typical organization and what are the patterns regarding roles and grouping of responsibilities. But the resulting question would have been torture for a respondent to go through, so I edited it down to the 15-ish responsibilities and roles you’ll see below, and I’ll probably have to reduce the number of roles further to make the question viable.
So, below are the questions I’m thinking of asking. Please use the comment area to suggest questions. I can’t promise to use them all but I can promise to consider them all and publish some of the more interesting results in this blog when they come in.
Today’s organizations must manage the explosive growth of all types of information while addressing greater-than-ever business demand for insights into customer needs and the business environment. Meanwhile, the significant regulatory and compliance risk associated with information security has increased the urgency for tightly controlled information management capabilities. These requirements are hard to meet, with scant best practices available to tame the complexity that firms encounter when trying to manage their information architecture. Enterprise architects must define the organizational capabilities they need to develop and evolve their information resources — as well as the technology to exploit them. You can only achieve all this with a coherent information strategy that defines and prioritizes your needs and focuses resources on high-impact goals.
How does an enterprise — especially a large, global one with multiple product lines and multiple enterprise resource planning (ERP) applications — make sense of operations, logistics, and finances? There’s just too much information for any one person to process. It’s business intelligence (BI) to the rescue! But what is BI, and how does BI differ from reporting and management information systems (MIS)? What is the business impact, and what are the costs versus the benefits? What is the appropriate strategy for implementing BI and achieving continued BI success? Our new report will give business and IT executives an understanding of the four critical phases of strategizing around BI to achieve business goals — or “everything you wanted to know but were afraid to ask” about BI. Here’s a sneak preview of the kinds of topics the report covers and the kinds of BI questions one needs to ask in order to build an effective and efficient enterprise BI environment:
Prepare For Your BI Program
The future of BI is all about agility. IT no longer has exclusive control of BI platforms, tools, and applications; business users demand more empowerment (or make empowered changes without IT involvement), and previously unshakable pillars of the BI foundation such as relational databases are quickly being supplemented with alternative BI platforms. It’s no longer business as usual. Ask yourself:
What are the main business and IT trends driving BI?
What are the latest BI technologies that I need to know about?
Join us at Forrester’s CIO Forum in Las Vegas on May 3 and 4 for “The New Age Of Business Intelligence.”
The amount of data is growing at tremendous speed — inside and outside of companies’ firewalls. Last year we did hit approximately 1 zettabyte (1 trillion gigabytes) of data in the public Web, and the speed by which new data is created continues to accelerate, including unstructured data in the form of text, semistructured data from M2M communication, and structured data in transactional business applications.
Fortunately, our technical capabilities to collect, store, analyze, and distribute data have also been growing at a tremendous speed. Reports that used to run for many hours now complete within seconds using new solutions like SAP’s HANA or other tailored appliances. Suddenly, a whole new world of data has become available to the CIO and his business peers, and the question is no longer if companies should expand their data/information management footprint and capabilities but rather how and where to start with. Forrester’s recent Strategic Planning Forrsights For CIOs data shows that 42% of all companies are planning an information/data project in 2012, more than for any other application segment — including collaboration tools, CRM, or ERP.
My colleagues and I have just completed yet another engagement with a large client — one of dozens recently — who was facing a to be or not to be decision: whether to move its BI platform and applications to the cloud. It’s a very typical question that our clients are asking these days, mainly for the following two reasons:
In many cases, their current on-premises BI solutions are too inflexible to support the business now, much less in the future.
The relative success of cloud-based CRM (SFDC and others) solutions may indicate that cloud offers a better alternative.
These clients put these two statements together and make the reasonable assumption that cloud BI will solve many of the current BI challenges that cloud-based CRM solved. Reasonable? Yes. Correct? Not so fast — the only correct answer is “It depends.”
Let’s take a couple of steps back. First, let’s define applications or packaged solutions vs. platforms (because BI requires both).
Subscribe to a solution-like CRM
Provide standard business functions to all customers (which makes it different from “hosting;” see below)
Difficult to tailor to specific needs
Usually are used synonymously (but incorrectly, see below) with software-as-a-service (SaaS)
Platforms for building solutions
Subscribe to tools and resources to build solutions like CRM
Provide standard technical functions to developers
Contain limited, if any, business application functionality
Usually labeled either as platform-as-a-service (PaaS) or infrastructure-as-a-service (IaaS).
As one of the industry-renowned data visualization experts Edward Tufte once said, “The world is complex, dynamic, multidimensional; the paper is static, flat. How are we to represent the rich visual world of experience and measurement on mere flatland?” There’s indeed just too much information out there to be effectively analyzed by all categories of knowledge workers. More often than not, traditional tabular row-and-column reports do not paint the whole picture or — even worse — can lead an analyst to a wrong conclusion. There are multiple reasons to use data visualization; the three main ones are that one:
Cannot see a pattern without data visualization. Simply seeing numbers on a grid often does not tell the whole story; in the worst case, it can even lead one to a wrong conclusion. This is best demonstrated by Anscombe’s quartet, where four seemingly similar groups of x and y coordinates reveal very different patterns when represented in a graph.
Cannot fit all of the necessary data points onto a single screen. Even with the smallest reasonably readable font, single line spacing, and no grid, one cannot realistically fit more than a few thousand data points using numerical information only. When using advanced data visualization techniques, one can fit tens of thousands data points onto a single screen — a difference of an order of magnitude. In The Visual Display of Quantitative Information, Edward Tufte gives an example of more than 21,000 data points effectively displayed on a US map that fits onto a single screen.