Every day, we all see articles and posts in our feeds about the "new world" coming from our growing stockpiles of data.
We hear phrases like artificial intelligence (AI) and machine learning and are promised that they are going to automate and accelerate business.
We're told how we can get predictive and prescriptive insights from our data to help us make better decisions.
We read buzzwords like digital transformation, Industry 4.0, and Web 3.0.
With this constant bombardment, you're probably thinking, "I'm pretty sure someone is just making this stuff up."
Most of all, for those of us not in a "Big 5" tech company, we wonder, "What does this have to do with my business?"
All the hype aside, the average person can look around and see that, despite all the fanfare, promises, and buzzwords, things haven't changed so much. Most of us are still using spreadsheets and largely the same data-entry software that we've been using for years.
So, what's the disconnect?
Well, it's like the old expression, "What got you here, won't get you there."
In the same way, all of our out-of-the-box or SaaS software products that helped us get to where we are today are now a big part of the problem keeping us tied down. Let's take a look and find out why.
Ever since humans first attempted to reconcile data from two different spreadsheets, we’ve asked ourselves, "Isn't there an easier way to do this?"
And thus was born an entire industry of software and applications designed to make it easier to collect information and then spit it out again for us to read and digest.
The value of such a function is so great that the average person now relies on over a hundred different programs to collect and manage data in their everyday life, personally.
Dubious? Just take a minute to think about it. Email, text messages, chat, office software, paychecks, bank accounts, credit cards, social media, podcasts, fitness trackers, rewards programs, retail apps, cloud storage, your car or "smart" fridge, etc. Each of these has a unique program designed to make it easy, or even automatic, to collect data and then reproduce it somewhere for someone to learn from.
Put a little thought into all the platforms and programs and apps that are connected with your life personally, and you can see that getting to a hundred of these is actually pretty easy.
If you’ve seen any versions of the "A Christmas Carol" movies, they always open with Ebenezer Scrooge diligently scribbling away at his accounting ledgers, reconciling the list of payments from one book with the account balances and interest accumulated from the other.
Even as miserly a character, he would have gladly made the required investment into a nice copy of Microsoft Excel or even QuickBooks . . . if only to save on ink and quills.
As wild a ride this has been and as rapid the progress we have made in under a century, we find ourselves faced with a whole new problem altogether. Having data collected by so many different sources about so many different things, there is now much more to learn about figuring out how to relate them all together.
Trouble is, we didn't think of that when we started.
We made software to track our income and expenses, we made software to track our employees’ productivity, and we made software to track our customers’ information, but did we think we would need to put all that together? Nope, not at first.
So today we sit on mounds of valuable data like a hoarder with a priceless antique collection, unordered and collecting dust in the basement. Information is only valuable if it makes a difference.
Much like how these original solutions were so valuable to us in the beginning in that they helped us collect data efficiently in small and specific ways, now the value of all that data together is exponentially greater.
Every major Fortune 500 company today is either built on this utilization of data or relies on it for making the decisions critical to its success. Making decisions based on your data is the primary competitive field for the next era of business.
So what's the answer for your business? How does your business compete in this new landscape? The challenge is that the solution for the previous era has now become the problem for the current era.
But all those discrete little software and applications we built in the past have made it harder to get the complete picture of data today.
When we think of data in terms of 1's and 0's, like how it’s stored on a computer, it’s easy to think of data as concrete and universal. But that is not how data is collected or used.
Data is collected with words, numbers, and strange labels we make up on the fly. And all those words, numbers, and labels come from human beings, not computers. Humans are much more inconsistent than computers.
Let me give you an example. Do you know what the word "terrific" means? I bet you don't, at least not what it originally meant. We use the word today to mean something good like, "That casserole was terrific!" (OK, no one says that about casserole, but you get my point.) However, the word comes from the root "terror." When you said something was "terrific" you meant it was "very scary." (Ok, now that I think about it, that can still apply to casseroles.)
When Oxford University set itself to the task of producing a complete dictionary of the English language, they thought they could do just that – make it complete. Taking over 50 years to complete, they came to the realization (probably in the first few years) that it could never be complete. The English language was too wild and chaotic for anyone to completely grasp, not to mention control.
Such is the way with data. Since much of data is words, it’s impossible to completely tame it. Even with numbers, are we talking about the Metric system or the Imperial system? Is that number a date? If so, do you use mm/dd/yyyy or dd/mm/yyyy? Better figure it out, because you have a meeting in either 30 minutes or 17 days from now. Is that 1254, $12.45, or $1,245? Are those numbers a pin number, the cost of a hamburger, or rent?
That's just the chaos of data itself. Now add hundreds of different applications, each collecting similar data, but each in its own way, its own format, and its own structure. Your accounting software stores all the dates at dd/mm/yy, but your ERP system has all the dates as mm/dd/yyyy. What. A. Nightmare.
"OK, OK, I get it. Managing data is like herding wild cats through a theme park. BUT WHAT DO I DO ABOUT IT?"
Hold on, I'm getting to that, . . . and stop yelling.
“The significant problems we have cannot be solved at the same level of thinking with which we created them.” ― Albert Einstein
Even though the dictionary, any dictionary, can't keep up with language 100%, it does do us one very important service: it gives us a common point of reference.
I may not know what my teenage daughter means when she says I'm "sus," but at least when my boss tells me to "put the kibosh" on something, I know he meant to stop it, not put it on a grill.
While we can't control all the language of the world, companies tend to develop their own internally accepted meaning for words used regularly in daily interactions, like "TPS Reports" (wink). Having these common "points of reference" is critical to communicating with each other, so it makes sense that it would work for data as well.
The start of any data strategy is first taking the time to think about your data. This is the people part. The people in your organization have to talk about the data they need, the data they have, and what they need to learn from that data. When was the last (or first) time you actually just thought about or discussed your data needs?
At Onebridge, we call this "data discovery," and the goal is to produce a high-level documentation of all your data, how it needs to relate to each other, and what you need to accomplish with it. This creates a baseline for everyone to understand and agree on what need to be done.
The next step is to think about the process. How is this data collected? How should it be formatted? What are the best names/labels to assign the data that we collect so we all know what it is? This is called "data modeling" or "semantic modeling" if you really want to show off. It's easier to talk about your data if everyone uses the same terms, and if data is easier to talk about, it’s easier to work on together.
The point here is that the thousands of software companies selling you hundreds of applications aren't going to do this for you.
In fact, they are going to make it worse because it's in their interest to do so. Company X doesn't want their data to work with data from Company Y, because they want you to buy all the software they have. X will only talk to Y when they are forced to by either market forces or regulation.
Just as the original problem of collecting and correlating data produced an entire industry, so has the problem of getting our data integrated spawned another industry.
But much like the early days of medicine, a surge in demand attracts the likes of both aspirin and snake oil. For every practical solution, there are ten "miracle cures."
You might have encountered some of the "miracle cures" for data yourself. Need to get your accounting software to talk to your project management software? Buy this plugin or add-on for another monthly fee. Need to get your ERP and CRM to share data? Subscribe to this SaaS solution by signing a two-year contract.
We're not saying these don't work at all, but they are like band-aids on a gushing wound. They don't solve the core issue with getting your data to work for your business. They just make it work a little "better." The real solutions get to the core issue: getting control of all your data.
Onebridge partners with a lot of great companies that do work to solve the core issue of giving organizations control over their data. Snowflake, Denodo, Alteryx, MuleSoft, Profisee, and WhereScape just to name a few. However, these are not out-of-the-box solutions; they are tools for working with data. None of them are right for everyone, and their benefits depend on your needs and situation.
If you have a communication problem with a special someone, the dictionary usually won't help. You need to go to a therapist or counselor who will help you understand each other and work out a method for effective communication. Such is the way with language, and such is the way with data.
Although, we call ourselves "consultants," the tools we use are the last step, not the first. Well, not the LAST step; we also make sure to provide training and guidance to help you get the most out of your data once you have it all together.
Accounting software helped make accounting easier (somewhat) and more efficient, but it didn't replace accountants. In some ways, it made accountants more necessary since it created more data, which required more people who knew what to do with it. The future of data is similar.
Data analysts are in more demand than ever before, and hundreds of new data-related job titles have sprouted over the last decade alone. Being able to work with, understand, and make sense of data is a growing field of study and practice.
Just like every business needs an accountant or CPA today after it gets to a certain size, so will every business need a data specialist of one kind or another. In fact, that is already the case. It’s just that the threshold for size versus need is shrinking.
All of this is to say that software isn't the solution to data, but in many ways it’s the cause the of the problem in the first place.
There will never be an out-of-the-box solution that solves the problem of taking all your data across all the departments and functions of your business and getting it integrated together to provide you the valuable insights you need. A piece of software that will solve all your problems is just a mythical creature today.
To leverage your data the way large enterprise organizations currently do, you need a data strategy. Our experience has demonstrated that most organizations we help end up using less software than they did when they tried to integrate their data on their own in the first place.
Like organizing your closet, the first step is often getting rid of all the junk that’s cluttering it up.
The difference between those large companies and your business is that they have a team of data engineers, scientists, and consultants working together to tame the data, bring it together, and then make it make sense. The other difference is that large companies have the money to pay all those people.
That's why we're here. We are a team of engineers, scientists, and consultants that you can bring in as needed to help your organization realize the promises of data in a way that makes sense for your business, your needs, and your budget.
We have deep understanding and experience with the problems organizations have with data. We know software alone won’t solve those problems. And that’s why we are a service-based consulting firm focused on data. Reach out to start a conversation, and we’ll be happy to offer guidance.