In the beginning, there was statistics. Ask any Ph.D. who produced a dissertation in the 1980’s, and, unless they were math or science majors, you will hear a litany of expletives about the statistical analysis chapter, during which their data results had to be “crunched,” using any number of formulae, and be crunched by hand, with only a calculator for a tool.
By the 90’s, of course, there were computer programs and it was a matter of plugging the right data into the right formula, and the numbers were automatically “crunched” to determine the statistical significance of a research study. Technology to analyze data has continued to evolve since then.
The Use of Analytical Tools by Startups
“We’ve come a long way baby,” as the saying goes, and businesses have all sorts of tools at their disposal to collect and analyze data that can help make better business decisions. Google Analytics, for example, is a great place for startups to begin and there’s a number of other apps to gather all sorts of insights for you.
However, as a startup scales, though, Google Analytics, and other tools (CRM software, Excel) will outlive their usefulness. It will be time to incorporate BI in order to make faster decisions based upon larger pieces of data.
And that’s really what BI is – software tools that automatically extract data from lots of sources and then extrapolate that data, so that trends can be identified and predictions made. Wouldn’t it be nice, for example, to extrapolate data from your competitors and find a gap in what they offer that you can fill? This kind of data you cannot get from the lower-level tools like Google Analytics.
What BI Offers to Startups as They Begin to Scale
It is commonly believed that AI is only for the “big boys.” Indeed, it is currently in use by the following:
- Banks are to acquire extrapolated data that will predict risky investments and customers who may default on loans
The medical profession uses it to gather and extrapolate data on a global level to predict outcomes of specific treatments and procedures.
Governments and the military use it to analyze and predict success (or not) of policies, legislation, and military operations.
And on a less serious level, some NBA coaches are using it to predict specific player behavior based upon past performance. Who is a great shooter but clutches? Who is a not-so-great shooter but who remains steady under stress?
Large e-commerce businesses use it to predict customer behavior, consumer buying habits, and the viability of new launches/products.
You don’t have to understand the data science behind BI. This is what many business owners believe and they thus feel inadequate to use it. It’s scary, and they don’t want to spend money on something they will not be able to use. But understanding the science is not necessary. Knowing how to use the tools is, and that can be learned. Here is how scaling startups can use BI to make sound decisions.
- BI will make predictive analyses based upon past data and trends. This will help an owner to determine whether a contemplated new product or service will be well-received and really marketable. Already, the fashion industry is using AI to predict design trends.
BI will provide far more analyses of your customers – their behaviors, those that are the most reliable and profitable, and those that may need additional attention. It can tell you who is dis-satisfied and provide possible reasons.
The right BI tool can analyze your utilization of resources and show you where you can save money on staff, supplies, etc.
Reports, information, graphs and charts can be accessed from anywhere by those to whom you give permission. And those permissions are important. Suppose, for example, you have “outsiders” accessing your site and you need to segment them out based upon their profiles. A solution for an Australian “pay as you play” recruitment startup, using BI-based mapping, “trained” its BI app to provide permissions based upon user profiles that were entered as either recruiters or applicants, along with other demographic data.
BI tools are primarily mobile-first now. Consider the convenience, if you are meeting with a bank or potential investor and can pull up your most recent data.
You and your team will be able to analyze all operations and pull KPI’s, so you can enhance what is working and re-model what is not.
Getting Started with Business Intelligence Software
There are a number of BI tools on the market and more being developed daily. Your job will be to examine the features that each offers against your needs. You have to determine what data will be most helpful, what you want to track, and what kinds of reports will be the most understandable and useful for you and your team.
Taking it one step at a time will allow you and your team to adjust. People, including yourself, buy in to new technology slowly. But understand that the impact for all of your operations can be significant – forecasting customer behavior, customer service, predicting viability of products/services, to name a few. But you cannot tackle everything from the beginning. Take iterative steps.
Do some learning yourself first – it’s not as hard as you think. Remember machine learning is all about the machine teaching itself from the data it is fed. And you can guide that learning by coming up with your own objectives and asking the right questions. This only takes a bit of practice.
A tool like IBM’s Watson, for example, will not break the bank and yet will allow you to play around. Feed it your data from Google Analytics and then ask questions about it. Gradually, you will learn what your AI machine can do for you.
Selling it to others in your organization does not have to be difficult.
- Take it to your team and simply demonstrate what the tool can do.
Show marketers how it can predict the highest trending topics and types.
Show customer service how it can extrapolate data to determine which responses are being received positively and those that are not and what other customer service initiatives are meeting with success.
Some will “power up” on the concept and can then lead others through their own excitement.
The important thing is to start small. You cannot expect to incorporate BI into every aspect of your operations all at once. You, as well as others, have to develop a comfort level, and that is a process. Start with small projects, show the successes, and the “buy-in” will come.
BI is a “Now” Thing
The future of AI is now. Businesses that understand its value and commit to a gradual but steady implementation will realize the unlimited possibilities it offers.
No one knows how far artificial intelligence will carry us. On the horizon are driverless cars and intelligent manufacturing. But for startups in any niche, its value is right now.