How Analytics?


[under construction — sorry about the mess!]

Don’t think about the data, think about the decisions

[illustration — skeptical executive, crossed arms… ]

It’s all relative

A lot of business analytics, naturally are based on the data you have available in the information. But this isn’t typically the information that executives need to run the business.

For example, if a company’s sales are down 15%, should the executives get their bonuses? Doesn’t sound like it, does it?

But what if I told you that the overall market is down 30%, and the closest competitor is down 40%? Now do you think they should get the bonus?

The important numbers in your business are always relative, not absolute.

It’s important to include appropriate comparison data in any analytics – across divisions, across time, or across companies.

[Beyond budgeting for a most more rigorous discussion]

What’s the Value of Analytics?

This is THE big question. The problem is that you don’t know what you don’t know.

What’s the value of ignorance?

How do calculate the value of what you don’t know?

Post-implementation vs. pre-implementation. If systematically say that you got more value than expected, then

Remember not to compare to zero benchmark – in fast-moving world, bound to be worse off after.

Go for customers, not internal staff…

Risks

How do I know if I have enough analytics?

Strong ROI means you should probably have more

Who should be in charge?

 

The importance of Rethinking

Design thinking…. Don’t know what they don’t know…

The holy grail of “self-service”

Everybody has always wanted “self-service” analytics.

There’s no truly self-service analytics solution. Somebody has always done some work in advance. The data has to be brought together at some point in a way that makes sense, and this has to be done by somebody with expertise in data and technology.

What has changed is the technology used, the data available, and the culture/expertise of information use.

There is now a lot more data available for business analysis, and a smaller proportion of that data come from internal corporate processes. While analytics has been around since the 1990s, the default assumption was that most of it would come from your own corporate systems.

The underlying data was too complicated for users to access in “raw” form, and a “semantic layer” of some kind was required that gave a business-friendly view of the information.

Increased analytic maturity (i.e. people are more used to manipulating data) and better underlying technology platforms (simpler, faster, iterative interactions with data are now possible) have reduced this need (but not eliminated it — indeed, it’s one of the biggest challenges of “governed data discovery”)

Although the tools were designed for self-service, many deployments of these solutions ended up being “report factories” where the semantic layer was used by IT/business experts to create reports for others. This often reintroduced the bottlenecks and frustrations that self-service BI was supposed to get rid of.

The ability to have fast, interactive feedback to new questions means that the user interface can be improved. Instead of forcing users to ask a question, get a result, and then use that result to make a chart, you can do much more of the interaction on the chart itself. It’s more appropriate for iterative discovery than reporting. It’s also easier and more appealing to use, which is an important consideration in a technology that is still, sadly, seen as a “nice to have” in more organizations — i.e. people are rarely forced to use BI as part of their job.

The Role of Governance

Data governance is “stopping people from doing stupid things with data”. And, sadly, human stupidity when it comes to data is a vast subject.

Data governance includes security, having people agree on common definitions (it’s amazingly hard to define something like “how many employees does the company have?” — and the answer will vary considerably depending on why you are asking the question/what you’re going to do with the data), bad analysis, wasteful recreation of the same analyses, etc.

Self-service BI can exacerbate all these problems by removing the checks and balances on data preparation and use. Without governance you are likely to end up with lots of silos of information, bad analysis, and extra costs.

There’s some nice literature on the problems of spreadsheet governance that is similar to the problems unsupervised self-service BI can lead to.

First define “success”!

Self-service tools can be very popular with business people that have been frustrated with the red tape and lack of agility associated with traditional IT organizations. To the extent that they can now make better business decisions faster, it’s clearly “successful” — but there can be tradeoffs in terms of long-term costs and complexity.

I’m going to take “governance” to mean processes and procedures in general.

The cultural aspects of information use are extremely important for BI— whether people are incented to use information, whether information is hoarded as “power,” who is responsible for information, how it impacts people’s bonuses, how the people involved in providing information to the business are organized, etc.

All of this is vastly more important than the technology itself. Frank Buytendijk has written a series of books that cover many of these people and culture issues in more detail.

De facto “governance” always exists, even if it’s not codified. The underlying principles around information culture are more important than having documented procedures, etc. And as noted above “success” depends on what is being measured, but I believe that the benefits of governance policies and procedures clearly outweigh the costs.

Success factors for Self-Service BI

Are there any factors that you would consider as critical when developing and implementing a self-service BI strategy?

One of the most interesting things about BI is that it’s ultimately indistinguishable from whatever we mean by “management.” I’m not sure there are any “critical” factors, but there’s a long, long list of best practice.

What do organizations have to change to take advantage of empowering their users to gain new insights through self-service BI?

Change the information culture

Components of traditional BI usable for self service

Self-service BI typically builds on “traditional BI” — i.e. the trusted data sources (finance, etc.) available through the enterprise data warehouse are a key part of the data analysis done in the “self-service” tools.

What types of components of traditional BI strategies would you consider for using with self-service BI?

There are many benefits to traditional BI. In particular, successful analytics requires at least some key information to be reliable, consistent, and secured. This often-painful process is one of the key areas of success of traditional BI implementations.

First, fix the people!

Poor Analysis Skills. Studies show that business people are not very good at analyzing data — more data can actually lead to worse decisions. A BICC with analysts on staff can help avoid problems, as can greater collaboration.

Bad analytics

Did you know that 99% of teenage suicides had listened to rock music in the week before their death?