Two presenters (Saroj from Standard Bank and Angela from Investec) discussed Business Intelligence Competency Centers to start day 2.

A BICC is a centralized team that captures knowledge, addresses technology, helps manage organizational change and defines common processes for BI across the organization. Repeatability is critical – not having to re-invent things for each new project.

When the BICC effort began Standard Bank found different areas of the retail business at different levels of competency – some were still at level 1 with little or no awareness of BI and others were at level 3 or 4 with BI becoming more strategic. A BICC was seen as a way to bring all areas up to a high level. Investec in contrast had a successful reporting tool project in one area that it wanted to replicate to others and saw a BICC as a way to do this.

Challenges that drove BICCs in the two companies included:

• A lack of collaboration between IT and the business
• A lack of trust and visibility
• Business people not empowered and forced to focus on the technology not on business decision-making
• No central strategy and lots of disconnected initiatives.
• Wide range of tools and technologies being used in different areas with skills and expertise scattered around
• No repeatability for BI projects

Starting a BICC required a typical set of requirements for both companies:

• An executive sponsor and some key stakeholders
• A mission for the center and a business case.
• Understanding of the BI landscape in terms of who was using what for what purpose
• Understanding of what the different business units wanted from a BICC
• A communication plan developed so that the business departments did not wonder what IT was up to
• PR – marketing and promoting the BICC.

Both BICCs have business user reps (focused on ROI and business requirements), BI analysts and developers (focused on efficiency and technology skills) and BI trainers (focused on skill levels with the standard tools across the board). The organization is virtual in both companies, with the business user members still reporting to their business managers while also reporting in to the BICC who report up to IT.

The business and IT people bring different skills to the BICC but they all need to be proactive, have people skills and bring some business acumen to the table. The BICC does not set business strategy or vision but tries to act as a catalyst for getting this defined and then provide a standard methodology, toolset and skills for delivering the projects that the strategy requires.

Recommendations:

• Start small but think big
• Inventory BI initiatives and skills
• Define business objectives for the BICC and figure out which executives to report to based on them
• Sell the BICC to new projects and make sure the funding strategy will work
• Look for ways to turn BI into revenue streams
• Work with other competency centers in the company
• Use reports with bad data, contradictory reports and other problems to convince people that a BICC is worthwhile
• Regulatory compliance is often a powerful driver
• Multi-skill the team so you can keep it small (10 or less) while still handling the range of skills you need

Several sessions this afternoon on data quality and governance. Rather than blogging these separately, here are some thoughts:

• Great illustration of data quality problem having a business impact – bad data led a Telco to prepare a large CapEx project to add bandwidth capacity but a physical inspection showed plenty of actual capacity. Bad data had led to an unnecessary plan.
• An example given was that 20% of customers generate 80% of revenue so a loss of 1% of these good customers through bad data might make a real difference. Of course, if you don’t differentiate how you treat customers then it may not matter if you are wrong about who the profitable 20% are! Good quality data only becomes valuable if it is being used to make a difference in business terms.
• Funding must be linked to strategic imperatives – show that better data is either necessary for an initiative or that it would boost the results of those initiatives. Data quality is not likely to be funded directly.
• A lack of trust in information undermines data-driven decision making. If people don’t trust it’s accuracy then they won’t use it, or analytics based on it, to drive their decisions.
• Suitable for purpose – which questions do you want answered, which decisions are you going to make, with this data? Use that to drive quality plans
• Analytics require data governance just as they require a level of data quality – it is hard to complete using analytics without governing the underlying data
• Regulatory requirements drive data quality, data governance – must be able to meet certain standards
• Drive the scope of your data governance program based on your data maturity, organizational structure/autonomy, external/internal influences/regulations, and the degree of executive support and drive – don’t get ahead of yourself
• Business must own and drive data quality and data governance – IT must act as a custodian of the data and nothing else. This, of course,is true of rules and decisioning too.
• Measurement, measurement, measurement – measure quality, measure governance, use your BI and performance management infrastructure to monitor these initiatives just like you would any other business initiative.
• Don’t forget to modify individual objectives and measures to reflect your data initiatives

I heard lots of talk today, in sessions and out, about how hard it is to get business owners to value data quality. My view is that this is inevitable and that the solution is to tie data quality problems to business value. And, of course, if you can’t tie a data quality problem to any business value then you should question whether it is really a problem…

Melanie du Plessis from the electoral commission was next. The IEC focuses on ensuring that elections are free and fair. One of the key elements of this is a focus on a single national register of voters that everyone appears on once and only once. Divided up the country into voting districts, to one of which each voter was allocated. Each contains voting stations within the targeted distance of voters. They began the whole project to define these maps. But now have a whole process to maintain and manage these units and this information can now be exploited in other projects – adding geospatial data to other BI projects. Increasingly their BI projects are being driven by the GIS system.

For instance, reporting on which parties got the most votes in each of the 19,726 units is hard to use when presented in a report. Present it through the medium of GIS and suddenly the trends and dynamics are much clearer. Use this view across time and the trends become clear. Some other examples of Geospatial BI applications at IEC include hardcopy maps, including the Atlas of Results, an interactive GIS desktop, a voting station finder for citizens and more.

These applications add a geographic perspective. For instance, when someone is looking for a voting station they can see if the station is in the target voting district while in the field. When planning elections, the geospatial data helps plan out the areas covered by supervisors and local officers etc. The status of preparing for an election is presented using geospatial data so trends in readiness or challenges in particular parts of the country are clear.

Critical needs:

• Spatial data, business data and a clear understanding of the links between them
• An understanding of the geography such as population density, terrain etc.
• Keeping data in synch with the geospatial data is tricky, especially as boundaries etc change all the time.
• Geospatial data can be very precise (for mapping) and more detailed than is needed for a business application
• Users want their maps to be pretty yet fast and functional
• Need an understanding of what will make sense from a reporting perspective to link BI to geography

One of the things I liked best about this was the drive to provide public access to all this data through the medium of maps and geospatial data as this makes it much easier for people to review it, something that is essential for public data like this. Combined with the trend to integrate these kinds of applications with location data on your mobile device, GIS is clearly here to stay. Personally I also see more and more use of embedded geospatial and location data – being able to run rules that are based on whether someone is in a particular region.

Continuing at BI 2010, Jon Hill spoke on the combination of BI and Performance Management. Jon reiterated the constantly changing environment and the tendency of companies to spend large amounts of money on data infrastructure without thinking through the way this investment will make a difference. In one extreme example he had seen one company spend $30M on a BI system that had just 8 users! It is not enough, he says and I agree, to formulate a strategy, you must also execute it. Performance Management systems should focus on providing the information environment that will allow you to monitor and manage this process. He had some great survey results:

• What companies want, but don’t get
  Change/uncertainty included in the process, integration of finance/planning with operations, not enough support for scenarios, not enough linkage to actions and programs.
• What they get but don’t want
  Strategies not linked to implementation, not transparent, budgets don’t reflect plans and objectives

Considering the stages in developing performance management systems, Jon argues that companies spend too little on assessment, a little too much on selecting their approach, far too much on monitoring and far too little on execution – sounds right to me, too much monitoring and not enough management.

To combine BI and Performance Management, he says, focus on decisions. To provide real value information must support the selection of actions that lead to positive outcomes. Absolutely! He also focused on the value chain of Data, Information, Actions, Outcomes.

Like me, Jon divided decisions into strategic, tactical and operational decisions and reminded the audience that they must know what kind of decision they are focused on. If we are using KPIs or strategy maps to manage the direction of our company we must understand the decisions we must make to deliver on this strategy, to affect these KPIs. We must ask "so what" and we must understand who makes the decisions that matter and focus on helping them do so.

To link BI and Performance Management then, you need to:

1. Build a strategic framework
   How is you information going to be used, what is your information culture so you can identify actionable information and be realistic about effective delivery
2. Clarify your operating framework
   Organization model, resources, governance, data and technology management. Where is the value?
3. Define core information
   Some metrics cross organizational layers, some definitions are constant. But others vary between organizations (marketing v manufacturing). Find the core information that drives the common metrics.
4. Get some quick wins
   Don’t wait for perfection, show business value quickly. Don’t lose track of the strategic view but keep moving and iterating.

Great presentation from Jon.

I have just opened ITWeb’s BI 2010 in Johannesburg talking about decisions and importance of decision making in making BI matter (I will post my slides later). Great audience, nearly 200 people with a strong showing from end user customers (75%) and, very interestingly, nearly half considered themselves business / IT straddlers which is a great trend.

The next session, although focused on BI, was very relevant to decisioning and decision management. Martin Rennhackkamp spoke on strategic BI and focused on a number of critical things that help you make BI more strategic:

• Understand the strategic business objectives, make sure you understand them and focus on how BI can support them
  This is absolutely critical in my opinion. If you don’t understand your business and how your BI investments can support it then you cannot possibly make good investments in BI. I particularly liked how he mapped business objectives to BI initiatives, showing how much of what the objective needs is delivered by each. This allows you to see which ones matter and where you have holes.
• Focus on crucial business processes
  Again, good advice and I liked his focus on operational processes. Not only should people identify the data that flows through those processes but they should also ask if the processes getting the information they need to make the right choices, the right decisions. Especially, are the people executing these operational processes getting what they need.
• BI to the masses
  It is critical for information to flow down to everyone on the floor – pervasive and operational. And important not to buy the tools without thinking about how this works.
• Master measure management
  Liked this phrase. Bring KPIs into the picture, linking KPIs to operational behavior and data. Understand how different roles need to consume information and analytics and how all this maps to company objectives and measures. Focus most of your effort on measures that the consumer of a measure has control over and to which they contribute. As I said once perforce, a dashboard should do more than just raise your blood pressure!

I recently spoke to an interesting company getting serious about analytics and looking for some staff. Drop me a line james@decisionmanagementsolutions.com if you are interested and I will forward your resume along:

• Data Mining / Analytics Business Analyst
  Senior Business Analyst with data mining and predictive analytics experience to lead the establishment of a decision support system that will drive major improvements in our business processes and marketing efforts. A key component of this platform is to mine our data warehouse to determine the analyst and client behaviors that drive profitability. To have an impact on our business, these insights and real-time recommendations will be integrated into our existing Business Intelligence (BI), Enterprise Performance Management (EPM) and Customer Relationship Management (CRM) platforms.
  This position will be part of the Technology Management team. The team consists of project managers and business analysts who manage a variety of technology initiatives on behalf of the business unit. They are responsible for managing projects from initial concept to deployment, working closely with stakeholders, including Research Analysts and management in the business unit, as well as IT colleagues in the development, support and training areas.
  This role requires a candidate with a strong background in statistical analysis, predictive modeling experience, writing business requirements and managing complex projects. The successful candidate will provide end-to-end project management from requirements gathering and analysis, through implementation, and post implementation support.
• Database Analytics Manager
  Database Analytics Manager to lead the establishment of a decision support system that will drive major improvements in our business processes, marketing efforts and enable the business to compete on analytics.
  A key component of this platform is to mine our data assets to determine the analyst and client behaviors that drive profitability and the Research report characteristics that can best satisfy our clients needs. To have an impact on our business, these insights and real-time recommendations will be integrated into our existing applications; such as Business Intelligence (BI), Enterprise Performance Management (EPM), Customer Relationship Management (CRM) systems and Research document Authoring and content portals.

Here’s my presentation from Predictive Analytics World, reproduced with permission from Predictive Analytics World and Rising Media.Analytic Journeys from Predictive Analytics World

I got a briefing from Quantivo recently. This is a company focused on behavioral analytics – uncovering patterns within the mountains of customer data that companies have – web analytics and point of sale data for instance. They help companies find these patterns, find the insight that they are not seeing with their current tools, and then help them make better decisions. Quantivo started with Retail (market basket analysis and promotion analysis) then expanded beyond that, including web analytics, and acquired new customers throughout 2009. The focus now is on their latest product release, Quantivo 4, and a strategic partnership with Webtrends (who now resell Quantivo). Quantivo has signed some good retail, B2B, marketing and insurance customers, including OSH and Cisco WebEx. Quantivo describe themselves as offering advanced analytics at scale in the cloud.

Quantivo sees companies trying to find who did what, when and why so they can target customers and promote more effectively. Companies want access to their data and effective answers without having to go through the IT department. The need is to democratize access to data and the answers hidden in data. People have a thirst for answers that is not being met by the BI infrastructure IT departments have implemented. In particular there is a gap between analytics and action – data is too far from decision makers who anyway can’t use the analytic tools that are available. Quantivo has tried to re-think the current data/ETL/Data Warehouse/BI/Data mining tool stack and do this re-thinking in the cloud to take advantage of the flexibility and elastic computing power available that way.

Their target user is a business analyst who wants to know things like who purchased movies and games together or what coupon users bought the 10 days following their use of the coupon, which campaign drove high-value repeat customers etc. A marketing analyst, for instance, trying to figure out what works and what does not. These are “advanced” analytics not because the questions are conceptually difficult to ask or because the representation of the answer is complex but because they are hard to answer using classic OLAP/reporting tools.

Quantivo 4 has focused in a few key areas:

• Dynamic Behavioral Segmentation
  Context filtering and context-specific queries (over a web session, lifetime of a customer, product range etc), multi-attribute segmentation and segmentation comparison
• Drag and drop web UI to make the solution accessible to business analysts
• Instant export so can load into some tool to take action using downstream applications

The web environment allows business analysts to create and manage worksheets (which can be shared between users). These worksheets can be built using drag and drop feature from lists of dimensions and measures in an OLAP-like way. Performance is good, with large numbers of records being processed quickly and filters can be easily added to restrict the data and see results. Within the results users can start to select elements (one department, say) and make them a comparison target. This allows them to see, for instance, what else people who bought from a specific department purchased at the same time. Or what people bought in the week following a purchase from that department.

Users can drill down, navigate around etc in an easy to use and pretty responsive interface. This is the kind of analysis most people would do in data mining or high-end analytic tools but made available in a very easy to use end-user analyst interface. These worksheets are live and updated when new data is uploaded and they can be shared across users. Customers’ data is uploaded to Quantivo, which is hosted on Amazon EC2.

At any point the user can take the population (of people who might be a good target for instance for an offer) and export to a marketing application etc. Quantivo makes it easy to access the result of a worksheet programmatically and they are working on more advanced APIs also.

Syndicated from Smart Data Collective

I often tell companies and other organizations that they should treat decisions and decision making as assets. In Smart (Enough) Systems, the book I wrote with Neil Raden, we said

Operational Decision Making as a Corporate Asset

If operational decisions must be made well for your organization to deliver on its strategy, they can’t be made randomly. They have to be made systematically. You have to turn operational decision making into a corporate asset you can measure, control, and improve. After all, when [customers] interact with you, they consider every decision you make to be a “corporate” one—that is, a deliberate one.

But is it reasonable to consider decisions, decision making, as an asset? After all an asset provides future value to an organization – tangible or intangible (goodwill and trademarks for example add intangible value while a factory adds more tangible value). Fundamentally an asset “contributes to future cash flow”. How does this work for operational decision making?

Operational decisions, those taken in a transactional context, include decisions like next best offer, pricing or discounts, product eligibility, claims approval, credit or fraud risk. Clearly each such decision has an impact on cash flow and profitability – good decisions having a more positive impact, bad ones a more negative one. The thing about operational decisions, though, is how often very similar decisions are made.

Consider claims – even a relatively small insurer (like Infinity Insurance discussed here) might receive 10,000 claims or more a month. Each claim must be considered and approved or rejected and making the right decision in each case adds to the bottom line. As a result the insurer needs a defined decision making process for claims – each one cannot be considered as a special case if 500 or more are to be handled efficiently every day. If the insurer has a good decision making process then each decision will be more likely to be a good one. If they don’t, less likely.

If we apply our definition then an effective operational decision making process is a form of asset – it contributes to future value by ensuring that better operational decisions are made. If we define the business rules, the analytics that make up this decision making process then we are investing in an asset. If we embed those rules, those analytics, into our operational systems and processes then we can ensure this asset is fully exploited.

In the book we went on to identify some characteristics typical of other corporate assets. Each of these can be applied to decisions and decision making:

• They are strategic
  Planning exercises and budgets should consider decisions – ensuring that plans that rely on changed to decision making, for instance, include the definitions of the changes needed. Executives don’t care about individual decisions but they should care about the decision process.
• They are managed
  The company invests in decision management and governance so that the quality of decision making doesn’t degrade over time
• They are visible
  The cumulative value of an operational decision should be known (multiply the difference between a good and a bad decision by the number of times such a decision is made) and the investment made in improving decision making should show up on balance sheets and be visible to management
• They are reusable
  Companies, well run ones at least, don’t duplicate assets or leave them idle. So with decision making.
• They are improved constantly
  Companies should invest in analytics and experimentation to constantly improve decision making – this is the equivalent of preventative maintenance and upgrades for machine tools.

High volume operational decisions drive your business every day, playing a role in every transaction. Investing in operational decision making will ensure that these decisions add, rather than destroy, value.

For more on decision making check out Thinking different with decision analysis by Ted Cuzzillo,  Tom Davenport’s article Make Better Decisions, this piece on Micro decisions for macro impact (references another Tom Davenport article), Prepare for Impact (Teradata magazine) and of course Smart (Enough) Systems.

Syndicated from BeyeNetwork

I am working with the folks at B-eye Network and sponsors Oracle, SAS, Aha!, Adaptive and Fuzzy Logix on some research – Business Analytics: Putting Analytics To Work.There is growing interest in the power of analytics, especially predictive analytics, to improve business operations. The use of data mining and analytic techniques in operational systems is moving beyond its early adopter base in financial services and into the mainstream. As companies adopt business analytic techniques they struggle with the balance between using these techniques to improve reporting and dashboards (“Predictive Reporting” as it is sometimes called) and using them to improve systems and thus every individual transaction (“Business Analytics” or “Decision Management”). A clear understanding of what business analytics are, how to use them, and the compelling business value of doing so is called for. Hence the research.

The study will describe business analytics and what should you expect from a business analytics vendor. It will discuss the motivation for adopting business analytics and how you should approach the evaluation of business analytics as well as how business analytics fit within an enterprise and business architecture. It will discuss risks and issues and describe the benefits and challenges based on real customer experience. Finally it will discuss the kinds of decisions thatwill show a positive return on business analytics and how business analytics can change businesses fundamentally.

All in all it should be a lot of fun to write and I am looking forward to completing it. In the meantime you can help by taking the survey – http://www.zoomerang.com/Survey/?p=WEB22A3HRGXRBS.

Look for the report in a couple of months on BeyeResearch.