Startups vs Small Business: Bimotics Perspective

Every once in a while I come across blogs and articles on the difference between startups and small businesses. The distinction between the two is that a successful startup is expected to grow exponentially from small beginnings, to attract customers rapidly, and hopefully grow to serve a very large market.  Startups are therefore a much smaller set of small businesses. In contrast, small businesses typically have a viable business and customer base in place and their needs are slightly different as a result.  Bimotics has been evaluating and analyzing this dynamic and this is reflected in the operational and financial dashboards that Bimotics builds.

My first impression was that the dashboard of a startup and a small business would be the same-- the slope of lines and the amplitude of the y-axis on some of the charts would be able to share the same visualization framework. Is it not true that all businesses should grow?  The trajectory of a startup would just be steeper right? At their essence, don't all businesses share similar foundational forces of accounting and operations?

While I'm not wrong, Neil Thanedar wrote in a 2012 Forbes article that specific focus of each business type would drive different analytics in a dashboard. He explains that small businesses are driven by profitability and stable long-term value, while startups are focused on top-end revenue and growth potential.  

Profit is basically calculated by subtracting Total Costs from Total Revenue. While Top-end Revenue is calculated by subtracting just Total Discounts and Returns from Total Revenue.

A small business focusing just on top-end revenue would be disastrous as it does not address the need for sustain long-term value. Profitability is a better analytic as it takes in account costs and gives a business owner indication of sustainability. As long as small business is making a profit, it will continue to survive.

Although cost is important when a startup in evaluating available runway, it is not used when focusing on how the business is performing.  Top-end revenue tells the founder what it takes to make sales. Against defined specific sales tactics, the founder evaluates what makes people buy and how to attract more sales. This insight is key for accelerated growth of a startup to capture that larger market potential.

Even amongst businesses of small size, it is important to understand how focus can drive different monitoring and analytical needs.  Although there are business fundamentals that all businesses need to adhere to, at the owner level, dashboards are not one-size fits all.

Bimotics is tailored to suit the needs and unlock the potential of companies from the emerging startup to the growing small and medium sized business.

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?

Bimotics – What’s In a Name?

On a recent trip to Silicon Valley, we got to have an interesting conversation with a so-called “cloud evangelist”, a charismatic character who preached the good word of all things tech and cloud solutions in particular. During the course of our discussion, he was interested in how we became so passionate about building Bimotics and how our unusual name came to be.  After hearing our answer, he encouraged us to share it with our customers.

Our Name

Bimotics stands for business intelligence automation. We got the name by taking the abbreviation for business intelligence, “B.I.”, and merging it with the word “domotics” which is the word for home automation.  We chose this name because it embodies what we set out to achieve -- automating the  processes required to bring fresh, easily identifiable, and actionable insights to companies. Efficiencies gained from this automation allow Bimotics to provide powerful yet affordable solutions to small and medium sized businesses. The same principle applies to our developer tools which automate the steps needed to build big data solutions.

Our Logo

The concept behind our logo is pretty cool, if not slightly nerdy.  As an analytics company, we wanted to show something of a bar chart, incorporating colors often used to denote visualizations like green, yellow, and red.  At the same time, we wanted to capture the heart of Bimotics analytics by depicting various data sources, represented by colored boxes, knit together. That’s our logo on the surface.

Looking under the hood however, reveals our inner geekiness.  Embedded in the logo is the binary code for “B.I.”. The yellow box is in the 2nd position representing “B” and the red and green are in 1st and 8th position which stands for “I” (the 9th letter in the alphabet).  

We are a technology company through and through, and it is reflected in our name, our icon, and everything we do! 

About our tagiline: Good to know!

The Definition of Self Service BI (in less than 100 Words)

Self service business intelligence (self service BI) is a technological approach focused on maximizing user capability to carry out analysis and obtain insights from a BI system while minimizing the need for IT experts.

Self service should allow users to easily gather data from software applications for import into an analytical database, then build metrics and/or visualizations, and finally consolidate them into dashboards and reports.

It should still allow business users to consistently make key decisions without long and costly IT implementation projects each time a new metric needs exploring. Today’s cloud and mobile platform are great means to deliver it.

This is Bimotics simple and easy definition of self servide BI, we use this everyday when building our products for the SMB. If you are interested in our service please sign up to try for our product beta.

Too Small for Business Intelligence?

When we first formed Bimotics, we saw a large gap between the variety of business intelligence options available to large enterprise and the slim pickings available to small and medium sized businesses (SMBs). We knew we could build a service that addressed the data analysis needs of smaller companies. As we discussed our idea with the community, many asked whether automated business analytical reports was really needed by small businesses. They were most likely envisioning “mom and pop” operations with time-proven processes that yielded predictable profit margins, and they would be justified for asking that question in that context. We, however, like to define these businesses as “micro-businesses”, not SMBs. 

Is business intelligence technology overkill for small businesses? I think not. Bimotics defines our target small and medium sized business segment as organizations with at least 20 employees and revenues of at least one million dollars. You see, teams of employees spanning multiple departments inevitably generate vast amounts of distinct data, making the need to organize and analyze this data imperative.  Here are a few points on how business intelligence can bring basic value to any small business.

Real-time Gross Margin

Understanding gross margin is essential to small businesses. Sales that rise disproportionately to gross margin can actually set a business back- requiring even more sales to make up for lost ground. Not only does gross margin show the relationship between costs and sales, but it indicates the quality of sales being made. Entrepreneurs and sales teams alike can over commit to making sales when they are hyper-focused on revenue goals and commissions.  The risk of this over commitment is truly real.  Keeping an eye on this key analytic as sales come in rather than at the end of a quarter can boost the discipline needed to keep gross margin high. Studying gross margin side by side with the spending profiles of your customers will help indicate which relationships your account managers should prioritize and develop, and which ones should be placed on the back burner. 

Cash Flow Projections

For start-ups in particular, understanding the length of your cash runway is critical.  Burning through cash reserves before product sales take hold will keep you grounded. Business analytics is a great way to help you monitor cash flow, as sales numbers, costs of goods sold, overhead expenses and goals are all major components of an accurate cash flow forecast.  You probably track these statistics individually, but have you seen them side by side in one place?  Have you interacted with them and seen how they look overlaid against each other? Visualizations, more commonly known as charts, and a business dashboards not only help keep track of your cash flow fluctuations but also help track the number of months of runway left and which cost factors are affecting your cash flow. Visualizations can illustrate the relationship between multiple data points so that you can get a real feel for your business. Keeping track of monthly cash flow is sufficient for making yearly projections, and visualization software helps you stay on top of your costs day in and day out.

As long as business intelligence and analytics software bring insight and cover essential analytics like gross margin and cash flow, small businesses can only benefit from the technology. It is not overkill at all.  Smart BI for small business works to perpetuate good business practices and helps you achieve optimum insight and efficiency.

Notes From the Road -- What We Learned at the Miami Small Business Expo

What a great week at Bimotics! The entire team got to update their pitching skills, but more importantly put faces and names to the businesses we work to serve each day. We spent two days traveling to small business exhibitions, talking to dozens of local and regional potential customers and fine-tuning our sense of what small businesses need.

Here are some key takeaways:

• People who understand the benefits of business metrics and analysis but “are not there yet” are great for validating our explanation of the value Bimotics brings, but most likely will not fit into the sales cycle we are targeting because it takes a while to set up and use standard business practices.
• Technology providers that cater to small business are asked quite often where to get analytics and business intelligence systems that fit a tight budget. Possible partnerships and cohort awareness will definitely help us generate leads and grow our business.
• Demand for business intelligence solutions significantly drops off when solutions reach the $10,000 price point.  Those with higher price tolerance tend to be service-type companies who use analysis as part of their main offering. Bimotics is pricing our service in line with other small business applications.  We also know that we can tap greater demand at a much lower price point.
• Although technical startups are interested in our analytical software, they tend to fall into the “innovator” rather than the “early adopter” bucket.  What this means is that if we focus on selling just to technical startups, we may not be able to get the critical mass of small businesses we need.  Our goal is to serve all businesses but word of mouth and trust will come easier from non-technical startups, or businesses that are slightly farther along in their development.

Ultimately we left the events with even greater focus on what we need to do to cater to different customer bases and understand how their needs differ.  Our first and immediate post-conventions actions are to schedule product demos with the businesses that expressed interest and continue building local relationships.

Of the dozens of people that we spoke with, all were inspiring.  They were happy to share their stories of success in surmounting challenges in their businesses. They did not complain about budget or about having assembled the perfect team before they figured out a solution.  We were left with a renewed sense of persistence and optimism towards entrepreneurship and providing analytics services that businesses will find invaluable.

So What is This Smart Data?

The last few months, I have noticed the term “smart data” popping up in sessions and blogs mostly related to big data and data science. The content is full of the value of using smart data to answer questions, but what exactly is it and how does it differ from the regular data we collect everyday? I did a little research and here are some things that I have learned. It turns out the concept of data marts and analytics marts that Bimoticsdiscussed  earlier this year are very similar.

According to Cambridge Sematics, a company specializing data and analysis, describes smart data as a set of data that has been collected in such a way that it can be optimized discovered, integrated, visualized, and analyzed. There are technology vendors that have described smart data as big data analytics or analytics on top of big data.

Smart data usually is collected from many different sources and applications. The data is then aggregated into a single location. While bringing it in, the different data elements need to be organized and standardized so that they relate to one another in a way that represents reality. For example, an order has many product line items and each product has a price. While this data modeling reflects how all data for business intelligence starts out, there are nuances that eventually makes this data “smart”.

To start, smart data is not a just a collection of all the data related to a particular subject. Instead, the information and data elements are evaluated for significance and the ones that are highly related to the subject are kept in the set. This means that the smart data model needs to be flexible so that data can be included and excluded as needed. I have seen this concept of smart data in articles related to customer insights and predictive analytics. Identifying and keeping only the most meaningful data points is at the heart of these kinds of analyses. 

The relationships between elements are defined and form a common set of terminologies, so that these sets becomes more understandable. Maintaining the meaning of the data and making sure that it has not changed from the original is key and often complex. The effort pays off when the number of false positives decline as well as when the data begins to be more  purposeful and can be used in many different ways to  answer many different sets of questions.

While, I am just beginning to understand the concept of smart data I am eager to learn more as this concept emerges into something as popular and common as big data.

How Business Intelligence will evolve in 2015

If you are in the enterprise technology industry, you probably know that innovation within the space can be a slow process. Companies that have invested many millions of dollars and thousands of hours in legacy technologies are often reluctant to make bold changes. While it may feel like innovation in enterprise analytics moves at  crawl pace, each year is not without its advances. Today we focus on the progress we predict to see this year in the space for business intelligence, analytics and big data technology.

Greater demand for sensor data analysis

With the onset of the Internet of Things, demand for the analytics and reporting of sensor data will continue to take off.  The use of sensors to take measurements and monitor activity has already skyrocketed.  Just as fitness trackers have managed to put another dashboard into your daily view, the need for solutions that process and display information from sensors can only grow. Even in industries such as commercial retail, the use of sensors like e-beacons, smart shelves, and intelligent fitting rooms are taking off and adding to the demand for systems that can handle big data sets and charts.  This sensor technology and its demand for data analytics is still in its infancy.

More mobile user interface solutions

At Bimotics, we are increasingly getting requests to be able to provide our dashboard on mobile devices.  With mobile phone screen sizes growing and users’ mobile savviness increasing, business intelligence and analytics will continue the evolution of dashboards on phones and tablets. I expect to see  sophisticated dashboard-applications take off,  building on the currently available charting user interfaces that have adopted responsive design.

Naming the flavors of business intelligence

This trend became more apparent last year. With the  exponential growth of data and its uses, I expect a further  “labeling of the flavors” in 2015. Big Data came first for nomenclature to distinguish large volumes of data from disparate sources. Smart Dataemerged as a method of using data that matters and using that data to build foundations for predictive solutions. Terms such as Dark Data, Operational Analytics, and Text Analytics are making their way into the vernacular. The labeling of the different flavors of business intelligence solutions will continue as this large and diverse industry becomes more mature, granular, and specialized in focus.

Even if innovation and advancement takes more than a year for full adoption to occur, things do change from year to year within the business intelligence and analytics industry. Looking beyond purely technical strides and advancements, these are the three broad trends I I expect to see unfold in 2015.

3 Reasons to Run Your Business Intelligence in the Cloud

With hundreds of thousand users now working on different cloud applications like Google Apps for business, Salesforce’s CRM, and Quickbooks online for accounting, it is clear that the cloud is the way of the future.  If your business analytics solution has not already headed skyward, it is worth considering the move.  Here are three reasons to run your business intelligence solutions, ie business dashboards, analytics, and KPIs, on the cloud.

Accessibility: Available On Any Device

Cloud technologies are moving towards responsive designs which enable dashboards to automatically reorganize information based on screen size. New cloud technologies adjust reports to present the most meaningful information based on the size of the device. Due to faster internet speeds, live analytics database connections allow for real time or near real time updates to business dashboards and analytics even on small devices. 

Since most of the computing power is on the cloud servers, the devices only require a simple browser to display the visualization or reports.  Cloud providers enable developers to support multiple devices, screen sizes and operating systems, in some cases even while offline. What is better than having your business metrics and info wherever and whenever you need them?

No Need for IT Resources to Maintain Massive Hardware Infrastructures

This is one of cloud technology’s biggest pluses. Cloud BI allow businesses to focus on running and managing their core offerings while leveraging applications and letting the service providers take care IT and the related resources needed to operate and maintain a technological infrastructure.  

Rather than draining resources on hardware maintenance, operating systems, networks, backup, storage, load balancing, and on-site data storage, businesses can instead shift their focus to the productive use of their data.  Google Cloud Platform, for example, provides offerings ranging from Platform-as-a-Services (PaaS) to Software-as-a-Service (SaaS), which encompass database, storage, and security management solutions, to name a few.

Access to Continuous Improvement and New Features

In order to make updates to non-cloud technologies, you need to take the application offline, install the patch or service pack, and finally test to make sure the update integrated correctly.  Updates on the cloud are a breeze in comparison. In fact, with the cloud’s continuous updates and improvements on the backend, product upgrades or fixes can be rolled out easily without huge interruptions or additional software purchases.  Continuous cloud updates translate to an enhanced user experience.  Google App Engine, for example, allows developers to push out new versions with a single click, providing users with instant updates the next time they use the service or application.  

There are many more benefits of running your analytics running via cloud technology. What benefits were you surprised by?

Building an Online Data Warehouse Part 3

Visualizing the Data

A picture is worth a thousand words.  No doubt, a well-crafted chart from sound data is similarly impactful.  An armful of those meaningful charts can lead to those a ha! “good to know!” moments that give your business that critical and much needed edge.

In our last blog, we explained that although marvin.'s own visualization engine is still in development, its analytical engine can currently be used for analysis by writing queries within the database. Soon, through marvin.’s user interface, or by leveraging other Google Technology Partners like Tableau or BIME, you will be able to create charts and visualizations on top of this database.  Doing so can help make sense of your data, identify patterns and trends, create reports, make measurements and ultimately help you learn and make decisions on how to improve your business- an ultimate cloud BI solution.

Currently, marvin. allows users to  configure databases in a way that is friendly to visualization engines available on the market. Databases can be set up to help data visualization engines recognize fields like text versus numbers versus dates and other options that help provide  meaningful insight. Ultimately, this visualized data can be combined to create dashboards where business users can see a tapestry of charts  rather than  one piece of data at a time.  Users will be able to define multiple dashboards, multiple charts, and multiple filters-- all based on their data.  

Visualizations conbimed with an online data warehouse open up a whole new world of business insight by allowing users to recognize and identify patterns that are almost impossible to see just by looking at numbers.  Dashboards with a whole host of these visualizations can provide you with critical information and rare insight about your business that you never had access to before.

Building an Online Data Warehouse Part 1

Google Cloud Platform is a great option for companies looking to build a Data Warehouse on the cloud, with plenty of APIs from which to build.  Currently, the number of APIs which you can choose from to connect your applications to the cloud is growing by the day.  As of now, you have two options: hire a developer to code and integrate on top of those great Google and Applications APIs, or use tools like marvin. to help you automate some of the processes that are required to build a data warehouse.

marvin. is a tool from Bimotics that allows you to set up an online data warehouse without the need to code. Google has created the capability to host the biggest data warehouse in history, Google BigQuery, and marvin. is the bridge you need to make that process as easy as possible.  The steps below will help you get started.

What does it take to set up a Data Warehouse on the cloud? A simplified process for cloud bi would contain the following steps: 

1. Gathering the Data

2. Storing the Data

3. Analyzing the Data

4. Visualizing the Data

Gathering the Data

This first step has historically been the hardest, but advances in on-premise technology and Cloud applications like QuickBooks and SalesForce now provide methods to extract the data through APIs. These are often referred to as connectors. Most of these apps allow users to extract data in formats like CSV or JSON. Even e-commerce platforms allow users to extract their customer, product, order, inventory, and lead information through these connectors. Extracting these files to build a Cloud Data Warehouse is where it all begins. Without access to raw data, it is hard to design and build the right Data Warehouse architecture. Remember the old mantra “garbage in, garbage out”, bad data will always yield bad insight or analysis. Important note before going any further: if your data has exceptions, missing items, or other errors, fix them in the application itself before progressing with your warehouse.

Storing the Data

Storage is another big challenge.  You will need to answer questions such as:

• Where should I store this data and how?
• Is there enough space on my servers and hard drives?
• How much data needs to be stored?
• How often does information need to be added?

After setting marvin. up, pull data from your chosen application (e.g. e-commerce sample). marvin. then allows you to create buckets or folders where these files are to be stored- each relating to specific customers, products, orders, inventory etc.

Organizing data into buckets makes it much easier to refer to later. We suggest you create two types of buckets for each file: one to process into the next step and another for files that have already been processed. For example, we have a file called “Orders_New” and one called “Orders_History.” Not to go into great detail on file names, but naming them as order_YYYYMMDD.csv can help identify when the orders where extracted.

marvin. allows you to store data in any format, but the next step requires that the data be either  in CSV or JSON formats. marvin. will take an additional helpful step in compressing the files into GZIP. This minimizes the storage space used as well as the cost associated with storing the data. Storing your data this way allows you to keep a good archive of your data. marvin. also lets you to upload multiple files or folders into a bucket. Finally, you can preview files, as well as download or delete the ones that you do not need.

In the next part of this series, we will go into the next two steps: analyzing and visualizing data using marvin. 

The next natural progression for Healthcare analytics

In building out our services practice, we have made a lot of headway consulting for small and medium sized businesses in the healthcare industry. With the 2015 government mandate to put electronic medical records in the cloud and make patient information more accessible to the patients themselves, we at Bimotics have seen healthcare providers exploring cloud based analytics options. Now that these providers have found ways to store records in the cloud, they are looking for cost effective methods to leverage this data for reporting and analysis.

With data already in the cloud, healthcare providers are seeing that capital costs needed for more administrative services can be minimized as spending for technology infrastructure such as hardware and structure can be left to the those providing cloud services. Cloud technology also benefits the patients with greater access to their information--even from different specialists and hospitals. With these increased efficiencies and accessibilities, the tangible evidence of cloud’s benefits is speeding its adoption in the healthcare industry.  

Adoption for healthcare based cloud business intelligence and analytics is not without its challenges and obstacles, however. HIPPA compliance, for example,  requires stringent privacy rules and has very serious repercussions when requirements are compromised. There is also much concern surrounding security and data integrity. Solutions can be engineered to address these concerns and we will go into the details of these technical solutions in upcoming blog installments.

Cloud-based healthcare analytics is definitely coming, and coming sooner than some may think.  Given the mandate to place patient records in the cloud and the associated advances enabling holistic patient care, healthcare providers and patients alike will demand equally cost-effective and accessible reports of the services they give and receive.

Related Blogs

10 Signs Your Business is Ready for Analytics Software

Data analysis findings in insurance fraud

The value of HMO analytics

In our last blog, we discussed how cloud-based healthcare analytics is gaining traction as medical records increasingly migrate to the cloud and managers seek new insights into workflow, waste, and administrative processes. This time, we take a further look at some key analytics  that utilization and finance managers in medical clinics and HMOs are leveraging to generate superior reporting and analytical capabilities.  Here are 7 ways business intelligence can enable comprehensive analytical clarity to HMOs.

1. What is my membership per member per month (PMPM), versus cost?

This metric is equivalent to income vs. costs in most businesses. It indicates how well you are managing your cost of services and how well you are positioned to sustain or grow. PMPM can be measured against both income and cost.

2. Optimize the overall HMO organization efficiency

HMOs are based on a capitation payment model where a group of physicians are paid a set amount for each enrolled person assigned to them, per period of time, whether or not that person uses medical services. Tracking and constantly measuring their costs and utilization is key to maintaining positive revenue.

3. Identify departments that are over utilized

By applying PMPM to a department or service, HMOs can compare their budgeted PMPM to their actual expenses. This helps them decide if their services are correctly balanced. Services that are not conducive to providing a fast diagnosis may not fit the capitation payment  model of HMOs.

4. Analyze usage trends to improve performance

Trend analysis of physician, pharmacy, laboratory, community program, hospitalization, and urgent care data allow HMOs to understand patterns and gain insights which in turn allow them to negotiate better rates or modify those patterns. Trend analysis helps managers to proactively  reduce costs and improve their businesses.

5. Monitor network allocation trends to drive customer satisfaction

By analyzing geographic and demographic information, HMOs can  provide a higher quality of services in the most cost effective way. Changing patient demographics can affect HMOs’ contract models, and it is important that they stay abreast of these changes with smart analytics.

6. Special program effectiveness

Special programs are key to HMO services.  For example, preventative programs can reduce inflated costs down the road.  Similarly, education programs can help patients respond to services better. Tracking these programs is key to understanding how they impact the HMO as well as which patients are benefiting the most and which still need to be targeted.

7. Physician, laboratory, pharmacy, radiology high quality of care metrics

Trend analysis can be applied to any department of the HMO. Following best practices that have been developed through careful BI analysis results in better services and increased cost effectiveness. For example, learning that a physician is prescribing all name brand medication vs. generics, realizing that hospital stays are extending beyond protocol, and determining that certain lab tests are not meaningful enough for a correct diagnostic are valuable insights. Monitoring trends help keep organizations knowledgeable as to what works well and what needs to be improved to boost efficiency and eliminate waste.   

Data analysis findings in insurance fraud

It has been just over six months since we began providing on-line analytical services to one of our customers.  Their business focuses on insurance fraud detection, and through the use of Bimotics, exceptional cost-savings have been achieved and valuable lessons have been learned.  This project continues to be a success, with high customer satisfaction and glowing feedback from our customer. We have been able to demonstrate such great benefits in such a short time that considerations are being made to expand our reach into other types of insurance plans and other insurance providers.

For this project, Bimotics built a product for processing, storing and analyzing a huge amount of insurance claims data (millions of rows and data fields), allowing for auditors to find trends, patterns, outliers and further detailed analysis of the individual transactions. Basically, we allowed them to cull through an ocean of data and raise red flags, pinpointing where potential fraud was taking place.  We leveraged our proprietary Data Mart model which allowed us to analyze multiple insurance companies within a single data model.  This significantly smoothed the learning curve for auditors allowing them to focus on the analysis and not on the different data nuances of each company. Finally, our HIPAA compliant system (medical record integrity certification) allowed auditors to securely analyze millions of records within seconds.

The results so far have beat expectations:

• Around $10 million in recognized savings after identifying the impact of claims for procedures that have limited medical value in unique scenarios.
• 1,000s of claims were wrongly adjudicated.
• Data Analysis can be used instead of on-site auditing because of greater efficiency with more results.
• Less than 5% of identified providers in the sample represented more than 80% of the total amount of auditable claims. This analysis allowed our customer to focus on personal audits with the most potential impact, reducing time and high cost audit resources. In this scenario, the audits were performed on less than 1% of the total providers list and maximized results.

Lessons learned from this Customer:

• The software-as-service business model made this project possible because capital investments were unnecessary, start up costs were low, and the customer could hit the ground running. The SaaS model plus consulting services allows business to start within days without the need for high initial cost and IT headaches.
• Automating the data cleansing process after identifying each new need had the greatest impact on decreasing turnaround time. Millions of records were imported, standardized and analyze within minutes, not hours or days.
• Collaboration is key, especially when the team of analysts are global or not in the same location. Secure web dashboards allow us to efficiently share and consistently communicate our findings.

Bimotics – What’s In a Name?

On a recent trip to Silicon Valley, we got to have an interesting conversation with a so-called “cloud evangelist”, a charismatic character who preached the good word of all things tech and cloud solutions in particular. During the course of our discussion, he was interested in how we became so passionate about building Bimotics and how our unusual name came to be.  After hearing our answer, he encouraged us to share it with our customers.

Our Name

Bimotics stands for business intelligence automation. We got the name by taking the abbreviation for business intelligence, “B.I.”, and merging it with the word “domotics” which is the word for home automation.  We chose this name because it embodies what we set out to achieve -- automating the  processes required to bring fresh, easily identifiable, and actionable insights to companies. Efficiencies gained from this automation allow Bimotics to provide powerful yet affordable solutions to small and medium sized businesses. The same principle applies to our developer tools which automate the steps needed to build big data solutions.

Our Logo

The concept behind our logo is pretty cool, if not slightly nerdy.  As an analytics company, we wanted to show something of a bar chart, incorporating colors often used to denote visualizations like green, yellow, and red.  At the same time, we wanted to capture the heart of Bimotics analytics by depicting various data sources, represented by colored boxes, knit together. That’s our logo on the surface. 

Looking under the hood however, reveals our inner geekiness.  Embedded in the logo is the binary code for “B.I.”. The yellow box is in the 2nd position representing “B” and the red and green are in 1st and 8th position which stands for “I” (the 9th letter in the alphabet).  

We are a technology company through and through, and it is reflected in our name, our icon, and everything we do!

4 ways to getting Sales Analytics through The Trough of Disillusionment

One of Gartner’s proprietary research methodologies is the so called “Hype Cycle”, which attempts to measure on a curve, where a technology or technological term is in its popularity maturity cycle.  The main components as you would expect are 1) the sharp rise as visibility increases and the technology is the talk of the town, 2) the peak, 3) the trough of disillusionment where the technology’s value delivered does not exceed the early excitement generated, 4) climbing the slope back upward as the hype wears off and the technology proves its worth, and 5) finally the plateau where the technology has matured and is in better line with consumer expectation.

Late last year, there was a lot of buzz about how Big Data is hitting its peak where its benefits and value are near-term overstated and expectations are highest.  After its rapid uphill climb, Big Data is for the first time looking down the other side of the slope and now it will have to prove value against dissenters and trek to the other side of the valley.  Information Week, had a pretty interesting article about it here.

What struck me the most was the steep descent of Sales Analytics and Gartner’s expectation that it will be mired in the trough of disillusionment for the next 5 years . I find this to be a shame as so much work and investment is going into sales analytics, and results thus far are not living up to the hype.  

Having worked in enterprise business analysis, I have seen firsthand the behavior, characteristics, and technological limitations of sales users -- and it drove the vision to start Bimotics.  With that, I have gathered insight about the subject of Sales Analytics and sales dashboards and have listed a few suggestions that may save the subject and get the group as a whole over to the slope of enlightenment!

1. Speed things up! - With the volume of relevant data and analytics, it is easy to make users wait for the information to load. Sales people are stereotypically impatient with shorter attention spans, so if you want them too look at analytics don’t make them wait.
2. Clean it up! - Sales dashboards and analytics should look as polished as the sales materials that sales reps give to to their customers. I found usability is profoundly important with this user group.  Do not expect them to click around to look for something.
3. Show the money! - Sales dashboards and analytics that don’t show the monetary impact are just not interesting to sales representatives who are generally incentivised by commissions and making a revenue quota. Blending information on commissions, pipeline value, opportunity size and bookings will certaining make the analytics more interesting and valuable.
4. Encourage sharing and collaboration!: When planning a sale to a Sales Executive, a rule of them is to highlight how easy it is to share and communicate. This is because being able to work as a team and communicate to others is very important to sales.  Analytics and insights from a sales metric should be captured in the tool itself and sharable with a team.