Business Intelligence / Analytics / Big Data Leader Job Description

Clients often ask Forrester to help them define a job description for a business intelligence (BI) / analytics / big data leader, executive, or manager. Here’s what we typically provide:

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Do You Have BI On BI?

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?
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Top 10 BI Predictions For 2013 And Beyond

It’s that time of year again, when everyone starts asking for the BI predictions for next year. Good news: We did a pretty good job on the last year’s predictions, and so there’re only a few reasons to update them. Therefore this year we’ll do the predictions in the following manner: base 2013’s predictions mostly on our one-year-old 2012 ones and then use the latest results from Forrester’s Forrsights surveys on BI and big data (as well as other Forrester research from the past year) to confirm or disprove the 2012 predictions and whether they still apply to 2013. If there’s room to add new ones (stay tuned), we’ll do so. So here we go:

#1 (From 2012 prediction #1). The best tool for each BI job trumps IT standards. BI has traditionally been ruled by overinsistence on enterprisewide standards and a single version of the truth. These will continue to be important, but they won’t be the Holy Grail. A purely standards-based approach to addressing most current business requirements is neither flexible nor agile enough to react and adapt to ever-changing information requirements. In 2012 (and now in 2013), expect IT to start embracing agile BI tools, such as ones based on flexible in-memory models, in addition to enterprise-grade BI tools and standards. For information workers who need information anytime and anywhere, agility concerns will trump standards.

  • Verdict. Thumbs up for 2013.
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Make data confidence index part of your BI architecture

I often see two ends of the extreme when I talk to clients who are trying to deal with data confidence challenges. One group typically sees it as a problem that IT has to address, while business users continue to use spreadsheets and other home-grown apps for BI. At the other end of the extreme, there's a strong, take-no-prisoners, top-down mandate for using only enterprise BI apps. In this case, a CEO may impose a rule that says that you can't walk into my office, ask me to make a decision, ask for a budget, etc., based on anything other than data coming from an enterprise BI application. This may sound great, but it's not often very practical; the world is not that simple, and there are many shades of grey in between these two extremes. No large, global, heterogeneous, multi-business- and product-line enterprise can ever hope to clean up all of its data - it's always a continuous journey. The key is knowing what data sources feed your BI applications and how confident you are about the accuracy of data coming from each source.

For example, here's one approach that I often see work very well. In this approach, IT assigns a data confidence index (an extra column attached to each transactional record in your data warehouse, data mart, etc.) during ETL processes. It may look something like this:

  • If data is coming from a system of record, the index = 100%.
  • If data is coming from nonfinancial systems and it reconciles with your G/L, the index = 100%. If not, it's < 100%.
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Craft Your Future State BI Reference Architecture

In the face of rising data volume and complexity and increased need for self-service, enterprises need an effective business intelligence (BI) reference architecture to utilize BI as a key corporate asset for competitive differentiation. BI stakeholders — such as project managers, developers, data architects, enterprise architects, database administrators, and data quality specialists — may find the myriad choices and constant influx of new business requirements overwhelming. Forrester's BI reference architecture provides a framework with architectural patterns and building blocks to guide these BI stakeholders in managing BI strategy and architecture.

Enterprise information management (EIM) is complex — from a technical, organizational, and operational standpoint. But to business users, all that complexity is behind the scenes. What they need is BI, an interface to enterprise data — whether it's structured, semistructured, or unstructured. Our June 2011 Global Technology Trends Online Survey showed that BI topped even mobility — the frontrunner in recent years — as the technology most likely to provide business value over the next three years.

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What Do BI Vendors Mean When They Say They Integrate With Hadoop

There's certainly a lot of hype out there about big data. As I previously wrote, some of it is indeed hype, but there are still many legitimate big data cases - I saw a great example during my last business trip. Hadoop certainly plays a key role in the big data revolution, so all business intelligence (BI) vendors are jumping on the bandwagon and saying that they integrate with Hadoop. But what does that really mean? First of all, Hadoop is not a single entity; it's a conglomeration of multiple projects, each addressing a certain niche within the Hadoop ecosystem, such as data access, data integration, DBMS, system management, reporting, analytics, data exploration, and much much more. To lift the veil of hype, I recommend that you ask your BI vendors the following questions

  1. Which specific Hadoop projects do you integrate with (HDFS, Hive, HBase, Pig, Sqoop, and many others)?
  2. Do you work with the community edition software or with commercial distributions from MapR, EMC/Greenplum, Hortonworks, or Cloudera? Have these vendors certified your Hadoop implementations?
  3. Do you have tools, utilities to help the client data into Hadoop in the first place (see comment from Birst)?
  4. Are you querying Hadoop data directly from your BI tools (reports, dashboards) or are you ingesting Hadoop data into your own DBMS? If the latter:
    1. Are you selecting Hadoop result sets using Hive?
    2. Are you ingesting Hadoop data using Sqoop?
    3. Is your ETL generating and pushing down Map Reduce jobs to Hadoop? Are you generating Pig scripts?
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BI In Russia And Israel

I recently had both the privilege and pleasure to do a deep dive into the cold and warm BI waters in Russia and Israel. Cold - because some of my experiences were sobering. Warm - because the reception could not have been more pleasant. My presentations were well attended (sponsored by www.in4media.ru in Russia and www.matrix.co.il in Israel), showing high levels of BI interest, adoption, experience, and expertise.  Challenges remain the same, as Russian and Israeli businesses struggle with BI governance, ownership, SDLC and PMO methodologies, data, and app integration just like the rest of the world. I spent long evening hours with a large global company in Israel that grew rapidly by M&A and is struggling with multiple strategic challenges: centralize or localize BI, vendor selection, end user empowerment, etc. Sound familiar?

But it was not all business as usual. A few interesting regional peculiarities did come out. For example, the "BI as a key competitive differentiator" message fell on mostly deaf ears in Russia, as Russian companies don't really compete against each other. Territories, brands, markets, and spheres of influence are handed top down from the government or negotiated in high-level deals behind closed doors. That is not to say, however, that BI in Russia is only used for reporting - multiple businesses are pushing BI to the limits such as advanced customer segmentation for better upsell/cross-sell rates. 

I was also pleasantly surprised and impressed a few times (and for those of you who know me well, you know that it's pretty hard to impress the old veteran):

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What Does R Integration Really Mean For BI Platforms?

I just received yet another call from a reporter asking me to comment on yet another BI vendor announcing R integration. All leading BI vendors are embedding/integrating with R these days, so I was not sure what was really new in the announcement. I guess the real question is the level of integration. For example:

  • Since R is a scripting language, does a BI vendor provide point-and-click GUI to generate R code?
  • Can R routines leverage and take advantage of all of the BI metadata (data structures, definitions, etc.) without having to redefine it again just for R?
  • How easily can the output from R calculations (scores, rankings) be embedded in the BI reports and dashboards? Do the new scores just become automagically available for BI reports, or does somebody need to add them to BI data stores and metadata?
  • Can the BI vendor import/export R models based on PMML?
  • Is it a general R integration, or are there prebuilt vertical (industry specific) or domain (finance, HR, supply chain, risk, etc) metrics as part of a solution?
  • What server are R models executed in? Reporting server? Database server? Their own server?
  • Then there's the whole business of model design, management, and execution, which is usually the realm of advanced analytics platforms. How much of these capabilities does the BI vendor provide?

Did I get that right? Any other features/capabilities that really distinguish one BI/R integration from another? Really interested in hearing your comments.

Key Questions To Ask Yourself Before Embarking On A Big Data Journey

Do you think you are ready to tackle Big Data because you are pushing the limits of your data Volume, Velocity, Variety and Variability? Take a deep breath (and maybe a cold shower) before you plunge full speed ahead into unchartered territories and murky waters of Big Data. Now that you are calm, cool and collected, ask yourself the following key questions:

  • What’s the business use case? What are some of the business pain points, challenges and opportunities you are trying to address with Big Data? Are your business users coming to you with such requests or are you in the doomed-for-failure realm of technology looking for a solution?
  • Are you sure it’s not just BI 101Once you identify specific business requirements, ask whether Big Data is really the answer you are looking for. In the majority of my Big Data client inquiries, after a few probing questions I typically find out that it's really BI 101: data governance, data integration, data modeling and architecture, org structures, responsibilities, budgets, priorities, etc. Not Big Data.
  • Why can’t your current environment handle it? Next comes another sanity check. If you are still thinking you are dealing with Big Data challenges, are you sure you need to do something different, technology-wise? Are you really sure your existing ETL/DW/BI/Advanced Analytics environment can't address the pain points in question? Would just adding another node, another server, more memory (if these are all within your acceptable budget ranges) do the trick?
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COTS Vs. Home-Grown BI Apps

Wanted to run the following two questions and my answers by the community:

Q. What is the average age of reporting applications at large enterprises?

A. Reporting apps typically involve source data integration, data models, metrics, reports, dashboards, and queries. I'd rate the longevity of these in descending order (data sources being most stable and queries changing all the time).

Q. What is the percentage of reporting applications that are homegrown versus custom built?

A. These are by no means solid data points but rather my off-the-cuff – albeit educated - guesses:

  • The majority (let's say >50%) of reports are still being built in Excel and Access.
  • Very few (let's say <10%) are done in non-BI-specific environments (programming languages).
  • The other 40% I'd split 50/50 between:
    • off-the-shelf reports and dashboards built into ERP or BI apps,
    • and custom-coded in BI tools

Needless to say, this differs greatly by industry and business domain. Thoughts?