This approach can be summarized as:

• start by understanding the business problem you are trying to solve;
• then determine the underlying business processes that either contribute to or are impacted by this problem;
• now we can identify the people who have responsibility for overseeing or executing these processes;
• then we can examine what decisions they make as part of their daily operations involving these processes;
• and finally we then ascertain what information, interactive analysis and analytics would help them make better decisions more quickly.

It is only after working through the above that we should look for the appropriate data sources to supply the required information.  Hence we start from the “outside” (the business aspects), and then move to the ”inside” (the data required to support improved decision making relating to those business aspects) rather than the other way around.   My strong belief that this “outside-in” is the most effective way to approach performance management.

As I’m writing this, the words of the Traveling Wilburys are streaming through my head – “Don’t it make you want to twist and shout when your inside’s out?”

Although the focus of the article is on analytics associated with big data applications, there are many applications for real-time and near-real-time analytics.  These include aspects relating to more efficiently conducting business transactions, including:

• before transaction analysis such as customer targeting based on behavior and demographic analysis;
• in-transaction analysis including dynamic offer making, fraud detecting based on correlation and pattern recognition;
• post-transaction analysis such as profitability analysis,  inventory ordering and supply chain management.

Analytics can also be applied to optimizing business processes and operations, including:

• Identifying the optimal channel mix and funding associated marketing campaigns and promotions;
• Balancing the product manufacturing mix based on projected demand;
• Optimizing the use of resources in a facility such as education or medical clinic.

I’m encouraged that the power of analytics is being recognized in the media as this will help build awareness and adoption.  As more companies empower their front-line workers with immediate, up-to-date data, interactive analysis and dynamic analytics, they will improve daily decision making, optimize their operations and deliver better business results.

• A list customers that placed an order in the last 5 days;
• A list of all students currently enrolled in a particular class;
• A list and quantity of products currently in backlog;
• A list of customers with payments outstanding for more than 60 days;
• A list of opportunities scheduled to close this week.

Another way of looking at this is that reports provide “what is” and “who are” type answers that are used to take tactical, operational actions.

Analytic dashboards are designed to answer multiple, related or more sophisticated questions and to provide greater context, historical trends,  future projections and statistical analysis in order to make decisions about how to improve operational and financial performance.  Some examples might be:

• How are the characteristics of the best performing sales people different to those who aren’t performing as well?
• What has led up to the current quality issues we are experiencing?
• What will happen to daily production if we continue to experience these quality issues?
• Why are our sales conversion rates lower than industry norms?
• When will we run out of cash at the current rate of cash burn?
• What would happen to overall gross margin if we could lower our COGS on this product line?

As you can see, there are “what will”, “what was”, “how”, “why” and ”when” type questions and are used to improve decisions and determine new actions and processes, rather than simply providing data to enable current processes and actions.

There is definitely a place for both traditional reports and analytic dashboards.  There are some simple guidelines that help determine when a report is the correct medium and when an analytic dashboard makes more sense, such as:

• If you have a large inventory of saved queries and reports, then you are trying to answer a number of questions that are probably related in some way and an interactive dashboard might make more sense;
• If you run a report and then export the results to a spreadsheet so you can apply more analysis, an analytic dashboard would be more efficient;
• If you run a report and then utilize a charting package to visualize or graph the results, it would probably be much easier to deploy a visual dashboard;
• If you are trying to visualize thousands or even hundreds of thousands of items in a dashboard visualization, then this would most likely be more appropriate for a report;
• If your visual dashboard has lots of large tables, then reports would more effectively produce these results;
• If your dashboard doesn’t have any analytics and is only used to view flat, single-dimensional data that is readily available from standard reports, and there is no interactive drill capability, then simple reports with embedded graphs would probably be more appropriate.

I hope this has provided some clarification as to the inherent differences between reports and dashboards.  Each has its own place in enabling every day business operations and in general:

• Reports are best at providing data to enable people to take operational actions; whereas
• Interactive, analytic dashboards are best for enabling people to derive insights in order to make better operational decisions.

• greater ability to get information on time;
• increased self-sufficiency and a greater percentage of power users;
• that users had greater capabilities to drill into detail, filter and explore data and tailor their experience.

This certainly mirrors what we perceive in the market and the capabilities we strive to deliver.  Previously I have described the need for BI to operate at “business speed“, personalization and the concept of interactive investigation of data.  All of these are key to delivering the requirements for rapid business insight within today’s organizations.  As Aberdeen says, the decision window is shrinking which presents a challenge to IT departments and visual/interactive BI addresses this challenge and  increases the business’ ability to meet the shrinking decision window.

Let’s start with a fairly standard trend dial showing overall product production:

Then by using the information menu, we can begin the process of extracting more useful information, perhaps starting with basic analysis such as min, max, averages etc as shown below:

Or we can begin to apply more detailed analysis such as trend lines, perhaps with forecast projections:

To give the following result to show how production might continue to trend during the month.  As shown below, the average daily production is projecting slightly upwards but not in a dramatic way.

We might want to look at a variance analysis against the budget or against the prior period or prior comparable period as shown below:

This makes it very clear when production is better or worse on a daily basis against a target, prior period etc.  In this case we are under performing against our budget on most days although we have had a couple of good days.  This might lead us to investigate further if daily production is relatively consistent from one day to the next and to do so, we might want to use more advanced statistical analysis such as a histogram or a control chart as shown in the sequence below, starting with a histogram:

And here are the results of the Control Chart:

We can see from the control chart that we are operating in a fairly tight range around the mean and certainly well within the Upper and Lower control limits.  This tells us the production process is consistent from one day to the next, but we are under performing against budget so we should look a little deeper into whether there is some way to isolate where the issue lies.  We might look again at the variance against budget, but expand along a relative dimension such as location:

When we do this, we can immediately see our problem is in Europe as shown below:

Now the story doesn’t end here, as we would once again look at trends, projections, control charts, break Europe production further by the various locations within Europe or perhaps by individual product lines etc.  However, this process of applying visual analytics is quick, easy and follows a natural path of question / answer that we are all very familiar with.  In a very short period of time a operations or business person is using analytic capabilities themselves to answer their questions, analyze a situation and make decisions on how to improve production to meet or exceed targets – all without requiring the assistance of IT, statisticians or business analysts.

The business world has similarly evolved to the point where vast amounts of data are readily available and tools exist to enable people to collaborate quickly and effectively.   Of course taking advantage of that data to improve business operations and results is not so easy. This is the province of business intelligence (BI) and there are lessons from our personal lives and interactions that help provide the characteristics of effective BI:

• Data must be immediately available or at least available quickly enough to support the particular decision you have to make;
• Data must be presented in a context that resonates with you based on your role and responsibilities;
• You should be alerted to new relevant data based on your own criteria;
• You should be able to quickly obtain different perspectives on the data in order to turn it into useful information;
• You should also be able to easily personalize the data presented and extract more useful information from that data by applying models, calculations and statistics;   
• As you develop your thoughts and hypothesis you should be able play out scenarios to see the projected impacts of those decisions;
• Along the way you should be able to collaborate and conduct a dialogue with others within the context of the data and your specific decisions.

We take most of the above for granted in our personal time online.  We use search engines to find information online, we can quickly obtain multiple perspectives through news channels, we get set alerts for relevant information, personalize the layout of the online services, easily communicate with others, view information multiple different ways etc.  The new generation of BI also delivers in the business world:

• Immediate data update and availability without having to wait for data warehouse loads, cube crunches, business analyst presentations etc;
• Role-based dashboards and reports provide information directly relevant for you and you can easily personalize your own portal;
• Alerts send proactive notification immediately a criteria is satisfied;
• Interactive, multi-dimensional filtering and expanding provides different perspectives on the data to show major contributors or root cause of an issue;
• “Every-person” analytics provides trends, projections, variances, histograms, comparables, statistical controls etc;
• Real-time scenario analysis lets you play out various changes to see the current and future impacts;
• Collaboration is facilitated through annotations, summary text and dialogues all delivered within the context of the data.

In summary the new generation of BI is very different and delivers value to the business world in the same way our personal lives have been enriched by real-time information, communication and collaboration.

• What is the most probable forecast for sales this period?
• To make this forecast what are the must-win deals I need to ensure close? 
• Are deals moving through the sales cycle as required to ensure they close?
• Which deals in the forecast are suspect or at-risk?
• Which reps should I focus on, based on must-win or at-risk deals, past history etc?
• How is my pipeline looking for the next period?
• What is contributing to the change in the pipeline?
• How does the pipeline compare to the prior comparable / sequential period?
• Which reps should I focus on from a pipeline perspective?

In order to answer these questions, you need information and insight, not data!  Your CRM system has lots of data, but how can you derive insights from all of this data without requiring lots of people and hundreds of reports?  First lets look at how data is turned into information by deriving additional data and applying analytics.

All of the derived data and analytics shown above can be provided “out of the box” by a packaged sales analytics solution built on top of a dynamic Business Intelligence platform.  The derived data and analytic provides answers to the questions posed at the beginning of this post, and this information is automatically updated and everyone can view the same information, filtered for their particular role and scope with requiring someone to build lots of reports and then clone them for each individual. 

 This allows sales managers, sales operations and individual sales to focus on relevant information, rather than spending valuable time trying to specify reports within the CRM system, and then not getting the information they need or the interactive analysis they require to obtain the insight to support better decision making. 

Even better is when people are proactively notified of events that require their attention, for example:

•  a rep might receive an email alert to the effect that an opportunity of value greater than $X that is forecast to close in the next 15 days has not yet moved past negotiation into purchasing;
• a sales manager might receive an email alerting them that their pipeline has decreased by $Y because a large opportunity has moved backwards in the sales cycle and the probability decreased.

Now these people are proactively receiving actionable information and they can react much more quickly rather than waiting for a operations person to run a periodic report.   Now we are really turning data into actionable information!

I’m sure Peter could do a better job of outlining the process he works through as we created and enhance the user interface, but I believe in essence it comes down to the following (this outline is within the context of a business intelligence solution but is reasonably generic) :

• Start with the target audience in mind;
• Outline a “day in the life of” that target audience;
• Use that outline to determine the types of decisions the target audience will use the interface for;
• Determine the work and information flow required to make those decisions;
• Structure the user interface to support the desired work and information flows;
• From this user interface and flows, work backwards to identify the information that will be required and the data sources that hold that information.

When designing the user interface as a result of the investigation above, there are certain principles that should be applied:

• Keep the user interface as simple and uncluttered as possible;
• Ensure consistency across the interface – of presentation, color, action items, layout and navigation;
• Provide additional levels of detail in response to a user request or in support of a workflow;
• Provide contextual orientation (show the navigation path to the information being viewed, provide comparisons to best practices or targets, show the filters, show the formula for calculated metrics etc.);
• Empower the user with addtional capabilities within the context of their work flow (in the case of BI, this means drilling, filtering, collaborating, applying analytics, evaluating secnarios etc.);
• Reduce the number of keystrokes and clicks required to view specific information or to take a desired action.

By following these principles we’ve produced a very well regarded user interface, but we believe there is much more we can do to enhance the user experience, proactively engage them and expand our rerlationship with them.

• Time is typically always a dimension – I might want to query for this month, last month, last quarter, year to date etc;
• Customer;
• Sales Rep;
• Product;
• Partner (if selling through or with partners);
• Marketing campaign (that generated the lead the converted to an order);
• Etc.

In each of these dimensions there can be a hierarchy, for example along the Sales Rep dimension we might have the following structural levels:

1. Individual Sales Rep.
2. Sales Team.
3. Sales Region.
4. Sales Country.
5. Global Sales.

So using a multi-dimensional data store and queries we could easily find the total of all orders for a particular product line that were sold in Quarter 3 in the Eastern sales region.  You may be thinking, “Hey I could construct a query in my CRM system that would give me that information.”  This is true – you could specify a query and run the report.  But what if I now fired off the following questions in rapid succession:

• How many of those orders were for product XYZ?
• Which customers bought those products?  Rank them from highest to lowest?
• That large order – which Sales Team sold it?  Which sales reps were involved in the sale?
• How much has that sales rep sold to that customer so far this year?  How much did they sell to them last year?
• What other products has that sales rep been selling?
• Do they sell with partners?  If so which ones and how much has the partner contributed?

If you were specifiying queries and running reports, I don’t think you would have kept up with me.  However an interactive user interface that leverages a true multi-dimensional approach puts the power of these types of questions directly into the hands of the person making the decision.  The ability to quickly traverse the dimensional hierarchy to ask questions and have immediate answers, helps to develop a clear understanding of a particular situation.

Some dashboards and analysis products are very flat, requiring you or a business analyst to specify queries on relational data and then use groupings and sub-totals in order to arrive at answers.  Or you might have to export the data into a spreadsheet and use mutliple pivot tables to derive answers.  Others only provide a single dimension based on time so that you can see historical trends.  But for “speed of thought” analysis, nothing beats the power and responsiveness of a true multi-dimensional approach.

Historically multi-dimensional data stores have had limitations because they had the concept of period “cube crunches” in which all of the dimensional intersections were pre-calcualted and stored to support rapid query performance.  This meant the data available in the multi-dimensional data store or cube always lagged the actual data in the transactional systems.  However, new technology now allows continuous update through the ability to incorporate new data into the multi-dimensional store in real-time.  I’ll describe this further in another blog.

1. Ensure everyone has access to timely information (this can vary from real time to periodic update depending on each metric characteristics);
2. Allow people to personalize their information  layout, content and presentation (within the constraints of security access); and
3. Empower them with relevant, easy-to-use and consume analytics to aid their understanding of the situation. 

I’ve previously described our approach to immediate data update in this blog and the advantage of personalization here.  I’ve also blogged previously about the concept of providing analytic views to everyone in order to help them better characterize issues and determine their cause.  Our approach had been to pre-configure the analytic functions associated with each Dial based on the characteristics of the underlying metrics, and allow these to be used simply by asking for an analysis view.

In the spirit of further democratizing Business Intelligence and analytics we are now taking that further and putting full control in the hands of the person making decisions, rather than the person configuring the dashboards.  When viewing metrics on a dashboard, decision making can be significantly enhanced by enabling people to interactively apply different analytics and immediately see the results, for example when trying to determine what the future results of a particular metric, the person might:

• Ask for a linear trendline, switch to a polynomial or moving average trendline to see which best fits the data;
• If in doubt, they could ask for error ranges to be applied to the trendlines to see which is best fit;
• Now they ask for the most appropriate trend line to be projected forward to see the forecast.

Or maybe the decision maker might want to examine different aspects of the data being presented, and could take actions such as:

• Apply a control chart to see how tightly the values are grouped around the mean to see if the process is controlled, or if there is a systemic problem;
• Look at a histogram of the values to see if there is a clustering effect that might indicate a root cause of an issue or where to focus their investigation;
• Apply a variance analysis against the target or budget to quickly see where and when the process is performing above or below expectations;
• Look at a comparison to a prior comparable or sequential period to see if there is a seasonality affect and whether the process has improved over time.

There are other examples, but I’m sure you get the value of providing this type of interactive analytic capabilities that can be used in an iterative cycle to quickly understand what is happening, why, what are the major contributors and how to improve results.

I’m excited about these enhanced capabilities and in a future post I’ll provide more details and some screen shots to show how easy it is for everyone to be empowered with powerful, intuitive, interactive analytics.