Ted Cuzzillo, writing at TDWI and citing Blake Johnson of Stanford, identifies 6 conditions for [or barriers to] the rise of business analysts:
1. The best analysts are skilled in three areas: First, they engage stakeholders and have an eye for business opportunity. Second, they inspire stakeholders’ trust with consistently excellent analysis. Third, “big data” requires skill with data management and software engineering.
This paints a similar but not identical picture to Drew Conway’s Data Scientist Venn Diagram. The key point of difference is that Conway places more weight on mathematical and statistical training, which is not the same thing as “consistently excellent analysis”, but is more important than is often assumed in enabling it.
2. Each analyst’s skills should be about 80 percent in data management and about 20 percent in business and analytics — but Johnson expects that to change over the next five or 10 years as tools make data management easier. Eventually the mix of skills will be the opposite: 20 percent data management and 80 percent business.
I have no strong view on this, but my intuition is that data wrangling will always consume far more time and effort than analysis. Analysis is a feedback loop and a read-write activity. Standardisation and automation continue to consolidate efficiencies but these tend to raise the analytical bar. That said, I’d be happy for future tools to prove me wrong.
3. Gaining a foothold within an organization is best done in small bites with an entrepreneurial approach. Forget trying for a “big bang,” he says. Instead, find a need and fill it quickly, then move on to others. Identify and solve one business problem after another — always making sure to keep your methods scalable.
This agrees with Analyst First’s contention, seconded by others, that the monolithic IT project approach doesn’t work, and that—within an existing organisation—a bottom-up Lean Startup approach is your best bet. The only exceptions to this are analytic-centric online startups and quantitative hedge funds.
4. Location of analysts’ workspace matters. They should work in a cluster for critical mass, which encourages sharing of best practices and support. If they sit within business teams, their work becomes more visible.
This makes sense. Isolated analysts are a problem whether they’re isolated from each other or from management oversight and direction. Generally speaking, senior executives need to be broadened while analysts need to be narrowed. Middle managers need to be skilled up to bridge between the two.
5. It’s an adjustment for everyone — on the business side but especially on the IT side. It means fundamental changes in the way data is organized and managed, and accessed and used, with both new technologies and skill sets.
6. Many IT pros deny access to data based on obsolete knowledge. Johnson reports that many don’t know about modern load-balancing and other technology that make such access safer.
Certainly true. I’ve written before about the data needs of analysts as distinct from traditional business intelligence consumers, and also observed that big data is at once driving up the need for advanced analytics and rendering traditional data warehousing approaches obsolete. But the odd part about the commonly invoked ‘IT vs business’ balance of power is the acceptance of IT as a ‘stakeholder’ as opposed to an enabler. It’s unquestionably the case that analytics doesn’t happen without software, but that’s just as true of accounting, graphic design, and most other activities conducted in front of computers in today’s workplace. It simply doesn’t follow that IT deserves, so to speak, a seat on the Security Council.
Cuzzillo closes well aware of both the future possibilities for Business Analytics, and the status quo political realities standing in its way:
You would think that both sides would sign up for the bargain the new middlemen [i.e. analysts] seem to offer. IT would cede control and concentrate on what it does best, managing the back end. Meanwhile, business stakeholders would get insights from these newly empowered, eager specialists. Analysts would be newly ready to answer business questions, conjure up new questions, and offer strategic options.
Analysts would colonize what had been the no-man’s-land between IT and business. Trouble is, the analysts may end up ruining the neighborhood for them. If the strategies Johnson suggests work, IT and business would find a new power growing alongside them. Analysts — simply from the position they would find themselves in, not from any wish to rule the world — would be indispensible, powerful, and well funded.
Who wouldn’t want that?
Related Analyst First posts:
- Analytics Education and Recruitment – Builders vs Finders
- Analysis is read-write
- Forrester on the need for agility
- Analytics Is… A Lean Startup Enterprise
- *Why Software Is Eating The World*
- The data needs of analysts
- Big data as an advanced analytics driver
- *Building for Yesterday’s Future*
- *The Elusive Definition of Agile Analytics*
I’ve argued before that most businesses continue to struggle with query and reporting, and that the increasingly common invocation of big data and social media in business cases for Business Analytics is a way of leveraging hype in order to fund second and third chances at getting these basics right. Beth Stackpole, writing at SearchBusinessAnalytics.com, quotes Brian Hopkins of Forrester on a related aspect of this phenomenon:
“Over the last 20 to 25 years, companies have been focused on leveraging maybe up to 5% of the information available to them,” said Brian Hopkins, a principal analyst at Forrester Research Inc. in Cambridge, Mass. “Everything we didn’t know what to do with hit the floor and fell through the cracks. In order to compete well, companies are looking to dip into the rest of the 95% of the data swimming around them that can make them better than anyone else.”
Stackpole’s thrust is that big data presents fundamental technical challenges to established data management practices:
This whole notion of extreme data management has put a strain on traditional data warehouse and BI systems, which are not well-suited to handle the massive volume and velocity requirements of so-called big data applications, both economically and in terms of performance.
Sources of big data identified in the piece include:
[T]he constant stream of chatter on social media venues like Facebook and Twitter, daily Web log activity, Global Positioning System location data and machine-generated data produced by barcode readers, radio frequency identification scans and sensors.
Social media, the Web, and GPS are data sources we’re conscious of interacting with everyday. It’s transparent to us as users of social media sites and smartphones that we’re consuming and generating digital information. The social processes which generate big data are more obvious and more novel to us than the business processes which generate ‘small data’.
Part of the significance of social media from a Business Analytics perspective, then, is that it has made business consumers of information far more aware of how much data they’re not able to get their hands on. The 95% that Hopkins characterises as having fallen through the cracks in the past did so less visibly. In the age of the smartphone, however, it’s much clearer how little data makes it into warehouses relative to what’s out there.
Seen in this way, big data is a two-edged sword for BI managers. It increases awareness and demand for information, and can breath new life into data management initiatives, but it also increases the dissatisfaction of business consumers because they’re now more conscious, and more frequently reminded, of what they’re missing.
Related Analyst First posts:
In my experience working for software vendors the answer to this has always been ‘yes and no’, but the one sure thing is that everyone uses Excel. Spreadsheets are the most pervasive and effective decision support tools. No organisation doesn’t use them, and it’s a safe bet that this will always be the case. No amount of data warehousing will ever be able to provide decision makers with all the information they need. To the extent that it can, those decisions can be automated. Decisions invariably require new data. That new data will be either unanticipatable, or tacit, or both. Spreadsheets are unbeatable for ad hoc data analysis and turning tacit data into explicit data.
Evelson poses his questions in the context of (presumably Forrester) research into BI Pricing, which he says is:
[S]howing a broad range of transparency (or non transparency) from BI vendors themselves. Some vendors welcomed our research RFI and are happily providing all the info we requested. Some are less transparent and are insisting that we only publish price ranges or comparative analysis (who’s more/less expensive) without showing their exact quotes. Yet, some others have declined to participate.
That doesn’t surprise me. Wide price ranges are both inevitable and understandable. Software businesses, particularly in growth markets like BI, concentrate more on increasing revenue than on managing to the bottom line. Costs just don’t matter as much. They’re also indirect – software being an information product. Part of the software sales process is working out what the prospect is willing to pay, which is basically what they’ll end up paying, which will vary from customer to customer.
Related Analyst First posts:
About usAnalyst First is a new approach to analytics, where tools take a far less important place than the people who perform, manage, request and envision analytics, while analytics is seen as a non-repetitive, exploratory and creative process where the outcome is not known at the start, and only a fraction of efforts are expected to result in success. This is in contrast with a common perception of analytics as IT and process.
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