Moving from spreadsheet marts to data marts

Currently every business, regardless of size, uses some sort of spreadsheet technology like Microsoft Excel, Apple Numbers or Google Docs Spreadsheets to analyze, review and report business operations and performance. They create tons of multiple versions of the same documents and the most sophisticated users generate spreadsheets using data from different applications, such as combining information from ERP and CRM systems to conduct analysis and draw conclusions.

This is a daunting task that is prone to errors due to its manual nature. Unfortunately, businesses seldom have a better choice or motivation to switch away from the way they have been running operations for years, which is particularly the case with small and medium businesses. There are several problems associated with running your business on spreadsheets. They are hard to maintain, tedious to update and their intended result can shift, losing focus of the original purpose for generating such representations of business data.

Businesses spend most of their time gathering data points, matching up information from different sources, cleansing the data from what you do not need, joining pieces and trying to understand how applications process this information to help drive decisions. A large amount of time is also spent fixing mistakes and validating what has been built. This leaves little time to actually conduct the necessary analyses and apply insights to actual business performance.

The common IT solution for “spreadsheet hell” is the use of data marts. A data mart is an access layer sitting on top of the data warehouse the gives data access to users so they can resolve most of the issues they experience with spreadsheets. Getting a data mart is not simple and requires a series of technical components such as connectivity, access, extraction, transformation, loading, cleansing, mapping and data aggregation at the source applications.

By using data marts, business users do not need to do any of the tasks involved in terms of maintenance and data massaging. Technology experts pre-develop processes to access and extract information from applications. Data architects then transform the data giving it business friendly meaning. The result is that any business user could use and understand the data. This proven IT process also verifies that the data matches and maps to other applications. Finally in data marts, only the most meaningful metrics are aggregated like amount, quantity, unit cost, hours and cost, enabling a better sense of the business.

Data marts do all the work so that business can focus more on analysis instead of dealing with the raw data. Reducing data silos, human error, and most of all the tedious, time-consuming effort is essential to making important business decisions: decisions that impact the bottom line. However, data marts are just one part of the puzzle.

In the next segment we will describe the limitations of data marts and how analytics marts and dash marts will be the next evolution in the progression of business intelligence. 

Is enterprise ready for Analytics Marts?

Observing consultants and IT organizations implement large business intelligence solutions, I find often that the first project either fails entirely or never gets past the initial phases. Lessons learned from these setbacks are often rooted in the client/ business not knowing the data they really want or political battles over which information and metrics are most important. Technical architects have avoided such battles by instead providing data marts, so that managers can help themselves to any data and build metrics in a self-service manner. By giving the client or business everything, the problem is solved.

But does this approach really help the business in the end? No.  Managers remain misaligned serving their best guess of what will get them praise instead of addressing the true business need. Internal to the organization, data proliferation occurs where meanings get blurred and maintenance is so difficult even labels lose their original purpose of a sufficient description. Data silos of big data proportions are saved per division which is wasteful and duplicative.

Generally, I am describing an enterprise problem. Small and medium-sized businesses (SMB) suffer less from these political problems as they cannot afford much system customization and the resources needed to maintain it. Instead the SMBs tend to stick with the standard and best practice fields and data points. Because of this, Bimotics can provide our customers a solution we call the “gallery of analytics”. This gallery hosts all the business analytics available given the operational and financial application data marts for which the customer has data.  Generally, these pre-built analytics reflect best practice operational and sales processes that are fundamental in all business looking to grow. The image below is an example of what the analytics gallery looks like. 

The value to business owners is that they do not need to know what metrics they want before they bring on analytics. Instead they pick and choose the available analytics that makes sense to answer a particular business problem. They also can prioritize these analytics based on the business strategy they laid out.  If the business changes direction, then the business owner can change out the analytics to reflect this new vision. Not having to go back to the drawing board saves precious time. This gallery approach to analytics puts the definition and the prioritization of metrics and at the end as well as provides breadth and flexibility to a manager.  

Can this same principle be applied to solve an enterprise problem? Although very complex to build, can an “analytics mart” based on only the standard fields and best practice processes of major enterprise applications such as SAP, Oracle, Microsoft and SalesForce be built using the similar principles as our “gallery of analytics”? This “analytics mart” would cut across the different platform so that advanced metrics are available. For example, metrics, like support center effectiveness, are shown as a blend of financials in SAP with support data from Siebel. This proposed approach solves one of the greatest barriers that keep enterprises from successfully implementing business intelligence, by defining what to measure up-front, avoiding the interdepartmental politics in its allegiance to only standard and best practice processes.

Why haven't companies implemented "analytics marts" already? The answer is twofold.  First building an analytic mart across enterprise systems houses many technical complexities especially when looking at all the software versions and system customizations that exist per enterprise. This is not to say that a solution is technically impossible however.  Second, budgets split by division and departments need to be continuously spent in full which enable data silo behavior over cross department collaboration and process analysis. In other words, large organizations have budgetary policies that encourage managers to make blind purchasing decisions.   How much of a fundamental shift would need to occur in an organization to embrace the sharing of data and metrics sharing?

The key to customer adoption of the “analytics mart” rests on a consolidated drive to improve your business and the willingness of managers collaborate holistically. Will enterprise managers have the courage to allow themselves to be measured against fundamental business process standards, in addition to evaluating how well their assigned divisions support the overall corporate strategy?

Participating in a shark tank

Since it was recently Shark Week, I felt compelled to dedicate one blog post to my experience pitching Bimotics in a shark tank. Investment groups, entrepreneur clubs and academic programs have all jumped on this trend of hosting business shark tanks. Active entrepreneurs that seek funding from outside investors these days, most likely will find themselves preparing for our actually pitching in a shark tank.

It is especially important that an entrepreneur to have their startup story crisp and solid.  I have found that talking to individual investors will be a be more inquisitive and casual in talking about your business. But in the tank, the story needs to fact based and all the latest numbers and metrics rolling off your tongue effortlessly.  Although passion for your business is important, there is less time to convey it.

The sharks in the tank may not be evaluating your business under the same criteria.  Some may be looking for only early stage business. Others could be looking for strictly B2Cs. Others are looking for that one story that is missing from their portfolio. This makes it hard to because you cannot tailor your message just the one investor or topic of interest. If you have the luxury to know the background of each perspective shark, I would just tell the story targeting the very most one you want.

It is rare that your startup will get all the investors to be frenzied over you. The businesses that do are special enough to have their story told on business insider. Just like a shark attack on the beach, its uniqueness is why its news. However, it just takes the one right investor to make it all worth it.

Arriving at the tag line, Good to Know

When we first started Bimotics, co-founder and I continuously refined our with the story and sought to find efficient ways to describe our business. We took the approach of talking to as many people as we can. Not just the target customer, but also potential investors and professionals. In all honesty, the task was amongst the most frustrating things we have had to do for Bimotics. To my defence, you try to explain what business intelligence is to a small business owner.  

Last year, I was traveling back from visiting my parents. I stopped at the Barnes & Noble to buy a paperback for my flight. (I generally travel with one tablet reader and one paperback; so that I can continue to read during takeoff and landing.) Since I was in the prime of my frustration, I bought, Words That Work: It's Not What You Say, It's What People Hear by Frank I. Luntz.  It turned out to be an amazing book that i couldn’t put down until arriving back in Miami.

Luntz tells us that a good tag line should be between three and four words. For business, it should conjure a positive emotion and be associated with what one could experience using your product. A good tag line could was also something that you could hear in everyday conversation.  

I sat through the flight listing different possible tag lines for Bimotics. I tried lines that would be associated with being empowered or becoming stronger and smarter. I tried clever and witty lines about digging through and using your data. Then there was an announcement on our flight saying that we were a little delayed a bit and what number our baggage carousel was going to be when we landed. The boy across the aisle me put down his iPad, took off his headset then leaned over to his mom and said, “Good to know.” IT WAS PERFECT!

Good to know is a common expression used when you learned something that will be particularly helpful to you. It is used to communicate appreciation, pleasure or relief about knowing something you previously did not. That is exactly the mission of this startup.  At Bimotics, our metrics, analytics and dashboards will point to revelations about a business’s operational health and financial performance. We want our customers to say “Good to Know!” when they use our software and learn something from their own analytics. Whether it is saving money, going after better markets or laying out a different operational strategy, our products can support these decisions.

Our tag line has been Good to know ever since.

Our marketing campaign: Essential Analytics

Recently, I just finished updating our first white paper for Bimotics’ Essential Analytics campaign. From concept to fruition, this milestone took much more time and effort than I expected- nearly a year. Much time was spent understanding what my potential customers, the small and medium sized business really need to know about analytics.  

After wading through topics about why metrics do not work for small business such as not having the know-how to set up metrics let alone time to look at metrics and having low data quality with the applications. (Problems our software also solves but makes us sound like IT folks.) I was wanted to focus on bringing value to those that were already open to metrics and analytics, but perhaps needed more practical advice on how to apply it. The response from potential customers that stood out and fit with my goal was: There is not enough context on when it is appropriate to use one metric or KPI.

As a practicing business analyst, gaining insight from the data and measurements comes more naturally. But observation has shown that many small and medium sized businesses do not have a seasoned business analyst to “read the tea leaves”. Even our early adopters, can struggle with making sense of the numbers. Once an organization has the ability to measure their business operations and performance. Questions immediately arise:

• Does this metric make sense for my business model?
• Does this metric make sense for my industry?
• Which of these metrics are more important?
• What does the analytics even mean to my business?
• Do these metrics even align with my current strategy?

From this research, I worked on the design for my for Essential Analytics marketing campaign. The white paper series will showcase one key business metric that one can get out of our first data sources: Quickbooks and Freshbooks. Then, we use business cases to describe how other managers and business owners use the metric to get solutions to everyday business problems. Our readers can get both more context around metrics and how they can be used, as well as, come up with ideas on how to use analysis to answer their own questions.

KPIs: Sales Representative KPIs

I have spent some time recently looking at Sales Representative Profitability and the types of analysis that can be done to make firmer business decisions. Another method is the use of KPIs. KPIs stand for key performance indicators which are measurements a business takes to monitor performance.

In setting up a performance based sales organization, managers and small business owners can create KPIs for their sales representatives.  According to the KPI Mega Library, there are a few related to the sales representatives. These can be categorized in 3 ways:

• Day-to-day sales activities:
• # of average appointments per sales representatives
• Time to answer a request by customer
• # of customers per sales employee
• Sales revenue measurements:
• Average sales revenue per sales person
• Total costs to gain a new customer
• Sales team performance:
• Average sales turnover per sales staff
• % of sales representative that met or are above quota
• % of sales representatives that have met sales target
• Total sales of sales staff / Total customers of each sales staff

It takes continuous measurements of these KPI to understand how well your sales team can performance as well as how much they can improve.  After several measurements you can start looking at trends. This is the easiest way to analyze the performance and changes.

Design Thinking: Observing Extreme Users 2

Continuing the learnings from the Design Thinking training, figuring out who will be the extreme users of Bimotics is similar to the hypothesis of our early adopter customers. It is not a stretch to assume that those that are currently measuring and analyzing business would be the the most likely early adopter and positive extreme user.  On the other side of the spectrum, the extreme users that are least likely to use our products are those with either low data quality as poor quality adversely impacts the effectiveness of analytics or hidden agendas that is not supported by the data.

The things I would like to observe from our extreme users that love our product and find value in our features include the categories of analytics that mean most to them.  Are they leveraging topics related to sales and marketing over supply chain and finance?  How are they using the customer flows we designed? To navigate to and from analytics and dashboards, are they taking necessary steps to get around? I have learned from other software entrepreneurs that small business “use copy” and paste more often than using “new” in order to avoid typing in the same information over and over. Finally, I would like to know on what devices they access our application. Our product is mobile enabled; and we have taken care to so that our infrastructure supports mobile.  I would like to know if it was worth it.

Something to observe related to the extreme users who don’t use or even dislike our product, it whether or not they go to their teams and applications and work to improve data quality.  Are there initiatives or trainings that makes their resources input better information?  For those whose agendas don’t have data to support their decisions, I would be very interested in seeing and understanding how communication is done to other team members and whether they get push back from the others with access to the same analytics.  

I’m sure there are other groups of extreme users.  And the identification of extreme users can extend past product users.  For example, those that read this blog would be not segmented by product customer buy rather by prospect or investor.

Design Thinking: Observing Extreme Users 1

Last week we had the privilege of attending a training called Design Thinking.  The training establishment, Experience Point, created the entire training session on the methodology of IDEO, one of the most distinguished design firms that have come up with innovative designs we everyday like the Keep the Change program at the Bank of America.

A bunch of blogs and startup readings, have been about getting out of the building and talking to customers.  At Bimotics, we have constantly talked with small business owners of all types about almost anything- our business idea, our tagline, our data sources we want to connect.  Sometimes we got an excited response and sometimes the person simply didn’t get it.  Getting to talk to anyone that will hear us out was always appreciated, but I can’t say the talks were always helpful. There was continued ambiguity on how to put what a business owner was saying in a context that made sense.

In the design thinking training, I learned the concept of observing the extreme users.  Extreme users are those that fall outside of the normal 80% if you are looking at a normal distribution. The extreme user is on both sides of the 80%. The first group represents the users that love and have completely adopted and live the subject you are studying. The other group represents the users that never use or care about the subject.  

Observing these groups is the key to getting real insight. You can see the difference between each extreme user. This can help provide context needed to get more quality insight. Being able to observe the right group requires a bit of profiling and a clear understanding of what you want to learn. Observing is not just having conversations with random users.  It is almost opposite, it is about consciously being objective and simply trying to understand how the user ticks.  When you have a conversation you may instill bias unintentionally.  

Looking forward to applying design thinking principles to Bimotics.

Analytics: Sales Representative Profitability 3

This week I wrote an analytics series on sales representative profitability. It is about the analysis and types of insight you get from measuring the profitability of your salesforce. Just from one analytic there can be several insights. It is about understanding what the data says and connecting it to the explanation of a business challenge.  Devoting one more post on the topic:

Salesforce profitability can often be described as an advanced sales analytic because it enables more complex analysis such as sales forecasting, predicting churn and sales campaign analysis.  

In order to measure salesforce profitability, you must be able to merge three types of business data over time.  

1. Sales Representative Information
1. Name
2. Tenure
2. Sales Data
1. Sales
2. Sales Type (Product/Service/Deal Type/Quantity )
3. Volume of sales by sales type
3. Financial Data
1. Revenue
2. Cost of Sales
3. Cost of Goods Sold
4. Profit over time

This type of analysis requires more than a regular query of a database or the building of a standard report to influence strategy. To do the analysis you many have to extract or download the data from your data warehouse or applications.  MS Excel or other business intelligence software will then have to used to carry out the analysis.