Last month, I reviewed the concept of big data, and why and how the preponderance of data now in our lives has the ability to impact our strategic direction, decision-making and technology choices. We need to be prepared with the right people to help us use it, and to make a point to use it in our organizations.
So, this month, I am going to talk about “small data.” This is an idea that I recently came across in an HBR article, “Sometimes ‘Small Data’ Is Enough to Create Smart Products” by Praful Saklani. The conversation advanced in this article is more along the lines of “more is not necessarily better,” but focused as “small, high-precision data.”
The idea here is that you may have a specific, specialized need – so you need to focus on the right data and not get overwhelmed with all the other data that is streaming your way. What this amounts to is not the 10,000-foot view of my last post – the all-important need to get yourself in line to use the data strategically across the organization – but the task-specific need that may or may not fall under a department within your organization.
Let’s not forget the overriding theme when we are talking about data: how artificial intelligence (AI), machine-learning and automated processes are the cause/result of the data. When we discover a process that is manual or too complicated for a machine, the light bulb goes off within the innovator’s mind with many “what ifs.” Whether focused on a product, a service or an organizational activity, the trend is flowing to reducing those manual tasks and automating as much as possible to increase productivity. So, the data show a change is needed, and then the data show how a new process is performing, allowing for change to be initiated and then measured and tracked once the change is made.
What does this mean to me today?
Start small if you cannot make the corporate focus. Take a look at the biggest problem in your organization and attack that first. We have seen the back-office account areas as the most wrought with paper or manual processes. Fix that first. Get an automated workflow in place and no paper invoices.
The ramifications of fixing one spot means you identify other areas where you need to improve your operations. OK. What is the next big problem? Ideally, you will take a step back and make a list of the problems and the areas and set a priority list for change. If you take two steps back you might be looking at a bigger solution that handles many areas. OK again. But, let your data or lack of data drive your business need.
Think about this: You are a manufacturer and your employees punch a time clock. Pretty traditional and mechanical. You must get some information that feeds your payroll and attendance system from that machine, but is it good enough? Let’s say you want to be digital. You see you can issue employees badges that are read at your entrances/exits so you know who is in the building and when. In addition to giving you more specific data as to arrivals, departures and breaks, you are now equipped to know not only who entered, but did not exit the building in a disaster situation. “Is there anyone still inside?” You would not only know that answer but you’d know exactly who it is. Information like this could save lives.
Those same badges also could be used to operate and release secure printing jobs from your MFPs/copiers, dispense food from vending machines, log in to computers, and restrict access to other secure locations within an office.
Other less dramatic needs could be:
- Your CRM may not track the campaign information you want, which leads to a need for better data, in general, to make the right marketing decisions.
- Your process for grabbing a customer’s payment history may not be in your database in real-time, and that leads to the need for a centralized repository the team can access a customer’s data.
- Your employees have to request a purchase order by email and that never connects to the actual issuance of the P.O. number nor the actual payment of the bill, which leads you to realize that you need a system and not a disparate set of systems that handle the request, issuance and payment tracking.
Fortune Magazine recently highlighted several of these smaller, precise-focused data needs that companies have implemented. For instance, call centers for several organizations use “AI that analyzes callers’ tone, tempo, keywords and grammar to route calls to agents with appropriate skills.” Their software solution reduced their call time by 23 percent.* You would never get this routing precision with an in-person screener for every call making that decision. Small need – “I wish I could get the agent best suited for each caller” – with significant gains.
Also, Three Square Market in River Falls, Wisconsin, just announced that they are going to be implanting smart biometric sensors from Sweden’s BioHax International in 50 of their employees so that they can give their service people easy access to vending machines for quicker service calls. No keys or codes will be needed to gain access to their machines. It is a precise need for a specific industry that will enable this company to improve their service and customer satisfaction.
I wish I could get our bills paid without a late fee.
It already seems like you’re losing money hand over fist when you are paying bills in general. Then, layer on top of that any late fees you incur because you don’t have your act together on the payment side. Is it the people or the tools? Is it important? There are many things to consider, but it will all depend on the priority you place on your problems and what the data show you.
ROI cannot be ignored. As noted above, the call centers reduced their call time – by a lot. That is an important metric for that aspect of the business. You might have others:
– Reduce onboarding time for new hires
– Reduce application review for student applications to college.
– Reduce the frustration of reoccurring patients refilling paper forms versus prefilling e-forms and the staff simply pulling up the information upon the patients’ visit.
Overall, we cannot ignore the data we have and the data we want to have, and we need to make some decisions, even the small ones, for process improvement. AI is driving our decision-making, but we have to embrace it and adapt it with our organizations to stay current and be smarter in our operations.
is a program manager at Konica Minolta Business Solutions U.S.A., Inc. and is responsible for program development with the company’s Business Intelligence groups, including the Enterprise Content Management (ECM) practice. Her responsibilities are to build sales and customer-facing educational and thought leadership insights as well as strategic initiatives for ECM.