Over the last year or so, we have worked extensively with Google Cloud’s premier offering, BigQuery. BigQuery was built because no traditional technologies at the time could perform fast enough to support Google Maps. It will be a key component of cloud business intelligence and big data solutions very soon. As a super fast API that works as an analytical database, BigQuery is such a different animal that we can’t help but continue to be fascinated with it.
7 fascinating things about Google BigQuery:
1. Ingests while serving data
BigQuery is read-only like other analytical databases. However,what differentiates it is that it can be fed data at the same time that it is ingesting data into a database. As such, multiple partitions are not needed.
Contributing to its mesmerizing speed is auto-optimization. BigQuery does not require the constant maintenance of indexes, as it stores data in a columnar-like structure. This makes processing data shockingly fast. It is amazing how it “just works”.
3. No size limits
The database size has no limit. This means that it can be as big as you need, which is just unheard of. We have tried this and it can easily handle terabytes of data. You can store all the data your business needs without impacting performance. This also means there are no servers or hard drives to manage.
4. SQL-like queries, easy to use/adopt
BigQuery uses a SQL-like query syntax? Yes. It is as easy to use as writing a simple select query. Given that SQL is widely used, this will open the door for more people to interact with BigQuery.
5: Ease of management
A simple cloud console allows you to manage the database objects like tables and views, but most important, you can secure the data assets within the data sets. Jobs history allows you to manage database updates, status, and/or errors.
6. Nested json, support for complex schemas
Don’t be fooled in thinking that something so fast can only handle extremely simple schemas. It actually supports nested json. BigQuery allows ingestion of the most complex structures, that are exposed via today's common web services.
7. Super fast
It can analyze billions of records in seconds, not minutes or hours or days like other databases.
Bimotics automates the ingestion process further making Google BigQuery and Bimotics a great combination. If this sounds too good to be true, reach out to Bimotics. In just a few minutes, we will be happy to show you a demo of these amazing capabilities!
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.
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?
The Bimotics team spent early July learning about Growth Hacking from Aaron Ginn at Stumble Upon hosted by Refresh Miami. I honestly had to Google what it meant, but I was excited to get perspective from the Valley nonetheless. I went in thinking that the principles sounded a lot like marketing so i figured it was just another buzzword going around the geeks to make the even more exclusive. But I learned that the Growth Hacking was much more essential than that. And indeed it has a place between product engineering and marketing that is focused on what matters most to investors- growth.
5 key takeaways from the event:
Growth Hacking relies heavily on Lean Startup Principles such as sprints and measures
User Accounting: new users + reactivation - churn - deactivations = user growth
When performing experiments, aways solve for the down case. So if the test case didn't work, then you have learned something. You can learn why it failed. True scientists are always trying to prove for the null
The goal is to find what are the people in your channel are thinking and how they are looking and interacting with your product
The data science role, assists product manager in determining areas to focus on by identifying new opportunities and communicating the long run effects of the A/B tests
For now, Growth Hacking is mainly focusing on more of the B2C business models and products that make money from ads and replay on shares and links. This makes sense as these applications need to get as many users (millions of active users) as possible clicking through their stuff. However, the basic tenets and lessons such as creating a metrics oriented culture, running a lean organization and focusing on actionable metrics definitely apply to B2Bs as well.!
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.
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.
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.
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!
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.