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.
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 marginis 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.
Cloud Business Intelligence (BI) refers to the hosting of business intelligence and analytical software applications on a virtual network or connected servers off-site rather than on a local or on-premise computing device such as a PC. Cloud-based BI allows users to consume key business information at any time, on any device, from anywhere in the world. By virtue of being “on the cloud”, the technology leverages the scalability and power of huge data centers, providing more computing power and better uptime performance.
Cloud BI applications provide businesses and organizations with the tools and services they need to analyze a full spectrum of BI-related data such as dashboards, KPIs, metrics and other business analytics.
While the list is growing everyday, cloud BI service offerings have traditionally included:
Extract, transform and load tools
Data cleansing & data quality tools
KPI definition and management
These tools have been optimized for web and/or mobile devices resulting in an enhanced user experience. Cloud technologies can be as secure as, if not more secure, than on-premise solutions and can provide the capabilities needed to offset internal resources and/or expertise.
Why Bimotics? Because Bimotics is a small company just like yours, working tirelessly to produce a product that will improve your business and how you think about your business. We firmly believe that we provide an easy-to-use tool that can help you analyze, refine, and grow your business in a way you never before thought possible.
How does Bimotics do it? In the same way that you have a special skill, be it making the best cupcakes in town, tailoring the finest suit, or running a high tech distribution network, we have a special skill as well. Your business is unique, and Bimotics is the unique solution to help you unlock your full potential. We have spent our entire careers figuring out how to bring you the best analytics solution as possible.
We have removed all the challenges that small businesses face when dealing with data through our a la carte menu of analytics. We have made the analysis of your business as simple as picking items off a restaurant menu. We have created a true self-service product.
Measure, monitor, and forecast your business at a glance.
Our skills are at your service. We look forward to your Good to Know! moment.
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.
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.
I am an avid follower of David Cumming's Blog on Startups. Any entrepreneur with a technology startup can benefit from a quick read of his posts, especially if you have a SaaS. The content that posts each week is both easy to read and often timely. Recently, there was a post on the questions entrepreneurs should ask themselves frequently. This is how Bimoticsanswered:
1. Why are we doing this?
@Bimotics our mantra is shine a light on the path that leads to that "Good to know!" moment. We truly believe that we can strengthen and improve small and medium sized businesses by giving them real-time analytics and information about their activities and operations- what we in the industry call business intelligence software. Our developer tools enable big data analysis as well.
2. Why is now the right time to do this?
The time is now for Bimotics because non-technical people have begun accepting and using cloud technology in their daily lives. Cloud technology has finally evolved to the point where thousands of people can access extremely vast amounts of data at lightning fast speeds. These two conditions make it the right time.
3. Why are we going to win?
We built a solution that seasoned developers said was impossible. We now have a track record of proving our naysayers wrong. We have a growing list of businesses that are signing up for our service even when others said that it would never happen. We are listening to these customers and steadily improving our product line. With our persistence and support from a highly qualified team, we are going to win.
4. Why aren't we doing even better?
Fund raising has been slow; and we need to sharpen the way we communicate our offering. We are improving how we reach the right people but we can do it better.
5. Why do employees want to work here?
Our team is excited about our position on the leading edge of innovation. They are also good people who believe that a robust and profitable business can flourish while maintaining a positive mission statement and the highest standards of ethics.
6. Why is this the right product?
From the very beginning, our analytics software has been tailored for the small and medium sized business specifically. We didn't simplify a product that was built for enterprise needs.
7. Why do customers love us?
Our customers have said that Bimotics has become an invaluable tool to them in their day to day business. That's "Good to know!", and we couldn't be happier!
In our year end post, we revisited some of our big wins in 2014. The beginning of a new year means goal setting and planning for what is to come. A new season means new opportunities and we are full of optimism! This blog entry focuses on those top goals for our startup. We are sure that the next 12 months will not be without pivots and turns, but this is just part of the journey we take and we are looking forward to the challenges ahead.
SMART Goal: 1 new product and brand launch this year
After much debate, we are considering changing our name to something equally impactful but easier to pronounce. When we chose Bimotics years ago, we were lucky to get a service mark and .com domain fairly easily. Both items can be hard to come by for a startup, especially since our name has a uniqueness of meaning. As we began pitching and discussing our company with people, we realized that “Bimotics” is tough for people to pronounce and read under pressure (especially while introducing our startup in front of a crowd). We are currently reviewing a short list of names and icons that meaningfully capture the essence of our core business as well as the name Bimotics does.
SMART Goal: Roll out to 2 core charting capabilities with ability to measure usage
At long last, we are excited to roll out our end-user presentation layer. We have heard it time and again from both prospects and advisors that UI sells. Our experiences show that people and customers expect a solid foundation and for the “plumbing to work” so to speak, but the way they differentiate the value of things is based heavily on visualizations. So while we consider the 2014 launch of our big data toolmarvin. a great milestone, we need to take this robust foundation and plumbing for analytics solutions a step further. In 2015, we will be rolling out a visualization layer that allows non-technical users to interact with Big Data easily and affordably and peek into their data in a way that they never have before. We believe funneling more capital towards front end development is what is needed to demo our true value while increasing revenue.
SMART Goal: Participate in 2 tech community events a month
We are going to help grow our local tech community. Bimotics has attended key meetups within South Florida in the past but this year we aim to increase our involvement. By being participants, organizers, audience members, and sponsors over the past few years, we have increased our understanding of the value a community of tech entrepreneurs can bring. Whether it was introductions to angel investors or the identification of helpful resources for product development, each event benefitted us in some way. We also have something to give back in terms of experience, expertise, and lessons learned along the road and we look forward to sharing our knowledge with the community in 2015.
So it is going to be another busy year at Bimotics. Of course there are several other more tactical goals for the year, but from a company perspective, we look forward to working to meet these goals in particular. None of these will be easy nor can be done individually. Let’s get to work team!
This month, you have been hearing a lot about the marvin. product launch. Here at Bimotics, it is a very exciting albeit dramatic time. We are thrilled to see how the whole team has come together to carry out all the tasks and steps needed for launch. Synergy is real. We are seeing it first hand.
To get a better sense of what we are bringing to market here is the transcript of a question and answer with our technical lead and co-founder, Roberto Landrau.
How did marvin. come about?
While developing our big data and business intelligence tool for small and medium sized businesses, in almost every instance we needed to import multiple files from multiple APIs, from multiple clients, during multiple time frames. The process was extremely tedious if not impossible without some sort of intelligent and automated ingestion engine that could get a handle on all this data. From this quandary, marvin. was born.
marvin. is super easy to configure and allows users to manage Google Cloud APIs like Google Cloud Storage and BigQuery. Before marvin., files could only be entered into Google BigQuery one at a time manually --great for testing, but not feasible for our SMB analytics product. Initially, we decided to build marvin. for internal purposes in order to manage the ingestion of our clients’ data. Almost immediately, some of our more sophisticated clients asked to use marvin. for their own Google Cloud Platform needs. We decided to make it a commercial ready product by adding few additional capabilities such as multi-user, account based settings, support model and subscription based payments.
What makes marvin. a cloud BI tool?
marvin. was born in the cloud, it scales to hundreds if not thousands of users seamlessly by leveraging Google’s App Engine. marvin. is a cloud BI tool as it ingests data from on-premise files or cloud files into one of the most powerful analytical databases on the market. Currently, marvin. focuses on the ingestion of files, but in the near future our gallery of API connectors and the integration of our visualization engine will allow users to store massive amounts of data as well as analyze and manipulate it simply through a robust graphical interface.
What makes it for big data?
marvin. is capable of ingesting data for multiple clients at the same time, and its storage capacity is limitless. There is no need to manage servers, or disks, or IT resources. Users can analyze billions of records within seconds, not days or weeks. Watching marvin. in action is truly impressive. With its parallel import capability and ease of use, it is the perfect tool for your big data needs. Also consider that SMBs can use this tool to gather data in real time from multiple applications commonly used to support day to day business, such as popular accounting and CRM software. Our ingestion engine also supports data in more complex formats like nested JSON, allowing businesses to overcome both big and small data challenges from a single location.
Who do you expect will use it?
marvin. is primarily geared towards Google Developers and Google Cloud Platform users. Non-developers that still work in with technology and IT will rejoice as there is nothing to code. Simply gather your data in CSV or JSON, upload the file and configure an automated import. Subsequent files will follow the same pattern.
Users with voluminous amounts of data and no time to deal with servers, disks and networks will benefit the most from marvin.. The UI is easy to use and wizard setup screen helps you to configure the ingestion within seconds.
Where can I find marvin. on sale?
marvin. is now available via the Google Web Chrome Store or at www.bimotics.com/marvin. Be sure to check out our animated overview video as well!
What’s with the lowercase m and the period?
Bimotics is very serious about the products we build, but our marketing team is also focused on one of the cornerstones of our company culture: fun. We certainly had a blast coming up with marvin.. marvin. stands for Massive Analytics Repository on a Very Intelligent Network, quite a name for a cute little robot. By going with a small “m” and the period at the end, the name transforms into an iconic symbol, a bold statement. Once you get to know marvin., you will surely love him. period.
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.
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.