It sometimes seems to those of us from the outside that large corporations with large data gathering and analysis functions just “are” – that they have always been this way. That is, of course, not true. Every company, regardless of its current size, had to evolve its data capabilities. Even your company may today have some basic data gathering and analysis capabilities.
It is helpful to create several “stages” of this data evolution. We do this for a couple of reasons. First, it’s important to have a scale – a growth chart, if you will. This scale lets you know where you stand in relation to complex data gathering and analyzing organizations. But we do this for another reason too. At different stages of this evolution, companies have different capabilities. We want to classify these capabilities so that any company – regardless of its stage in the data evolution process – can take advantage of data-driven processes.
Stage 0. Little to No Data Gathering
Of course, everyone has to start at the beginning. Initially, companies gather little to no data on their processes. Every company has a little data – basic accounting data is necessary to stay afloat. The Stage 0 company is characterized by two criteria:
1. The Stage 0 company does not possess much data beyond payables and receivables, and
2. The Stage 0 company recreates data when needed to answer a business question rather than collect it in advance.
In other words, the Stage 0 company collects little or no data on an ongoing basis. This isn’t to say a Stage 0 company doesn’t know its business. On the contrary, in order to be successful any business needs to have a good sense of sales cycle times, the type of prospects most interested in buying, the products or services that are most profitable, and approximately how much they should charge for those products and services.
But that’s different than asking what the sales cycle times were for the last 5 sales. Or the last 50 sales. Or asking what the profitability was for the last 50 customers.
In addition, many companies are able to figure this information out from records, recreations, and research. But Stage 0 companies have not collected this information in advance, getting ready to answer such questions before they’re asked.
If you identify yourself as a Stage 0 company, that’s okay. Don’t worry, you’re in good company. Being a Stage 0 company doesn’t mean there are no opportunities for you to take advantage of data-driven business approaches. In fact, this book will outline very specific things you can do today to start turning your business into a data-driven business.
Stage 1. Basic Reporting
Companies that collect data typically report on it. The Stage 1 company generates this basic analytic tool: the report. To be clear, a report is simply a summary of collected data, perhaps even some basic statistics behind that data, such as averages, totals, minimums, or maximums. These are very common in most companies: sales activity reports, prospect summary reports, sales projection reports, cash flow reports, manufacturing reports, etc. The Stage 1 company, however, is characterized by the lack of formal analysis of these reports. In other words, interpreting of these reports is left to human beings.
Now, there’s nothing wrong with human interpretation. In fact, human beings can see patterns in data sometimes that computer programs are not capable of finding. The important criterion of a Stage 1 company is that there is nowhere else for this data to go.
Of course, there’s a lot of opportunity once the data is collected and stored. In this book we’ll specifically discuss those next steps that Stage 1 companies can take in order to use the data they have.
Stage 2. Trending and Forecasting
Once enough data is available, and a company has the appropriate tools in place, historical data can be used to help find patterns and potentially predict future outcomes. The Stage 2 company uses their data to forecast trends and predict outcomes.
To be clear, these classifications and predictions occur in an automated fashion with calculations and procedures. It’s not enough for a Stage 2 company to rely on human interpretation alone. A Stage 2 company can tell you what next week’s sales forecast is, along with a margin of error for how confident they are in that number. They can tell you how long it takes to process an order or how much time will be spent in service or installation.
In order to know this, data has to be collected over a reasonable period of time. How long is reasonable? Well, that depends on your industry and the type of business you do, but later in the book, we’ll cover a few ways you can guess how long is “long enough.”
A Stage 2 company gets very good at predicting outcomes. They don’t often get surprised by the regular ups and downs of business, because they’ve been tracking “normal” for some time now. But as good as the Stage 2 company is at predicting outcomes, they can’t seem to influence them with any regularity. That’s where our next stage comes in.
Stage 3. Inferring and Classifying
It’s one thing to know that you’ll sell 100 widgets next week. It’s an entirely different thing to know that if you lower the price by 10%, you’ll sell 150. The Stage 3 company knows this because they use their data to infer relationships and classify influences.
Inferring relationships requires us to go beyond predicting outcomes and study the inner workings of why things happen. What makes our sales figures go down in November? Which customers are likely to pay more for our product? What combination of product, line, and staff create the biggest likelihood of delay in manufacturing time?
These questions require us to stack data up against other data and see if there’s a connection. Over time a Stage 3 company can tell you not just who their most profitable customers are, but why. And they can use that information to find other more profitable customers.
The Stage 3 company can produce lists of influences of their outcomes, or key drivers. These key drivers can influence numeric outcomes, like profit, or non-numeric outcomes, like: did they buy or not? These key drivers help guide decision making. When a senior leader or executive makes a decision to stage strategy, the Stage 3 company can use these key drivers to get a sense of how outcomes will react to that new strategy. Because they can do this, simulation is often a key decision making tool.
However, a human being is still using instinct to guide their strategy. True, with a list of key drivers and an idea of how they affect your business, you can simulate different strategies and see how they will work. But can you simulate every strategy to see which one is best?
A simple example will prove how that becomes difficult. Let’s assume you’re a clothing company that makes 3 styles of shirts and 3 styles of pants. The shirts and pants come in 3 different colors and 3 different sizes. You have only one manufacturing process for the clothing and you need to decide how much time to spend on each style of shirt, pant, color, and size before retooling the process for the next. And of course, each style of shirt and pant has a different level of profitability associated with it. Oh, and you can’t make enough of everything to meet demand; you’ll have to pick and choose.
Even if you know exactly how many shirts and pants will sell and at what price, with all the different combinations of shirts and pants and colors and size, and the tradeoffs in profitability between them, how do you try every combination?
Those who have a little bit of mathematics background may recognize this setup for a type of math problem called “optimization.” This type of problem is solved routinely by our late stage of company.
Stage 4. Optimizing
Optimization is the idea of getting the most (or least) of whatever outcome you want: usually profit. The Stage 4 company is able to find the maximum or minimum of what they want by scanning over all possible scenarios that may influence their outcomes.
For example, in the situation above, a Stage 4 company can tell you exactly which shirts, pants, size, and color combinations will maximize their profit, given the restriction they have on the manufacturing process. They can also tell you exactly what price to charge and which customers to target when. They can tell you which processes are most likely to meet their strategic goals: reduce cost, increase innovation, or add to the bottom line.
It is true that in order to achieve optimization, one needs to have a good toolset, good data, and good skills. But it does not mean you have to be a Fortune 100 company. Even small companies can use tools to optimize their processes with very little data, as long as the infrastructure is in place to measure response and calibrate your efforts ongoing.
Evolving
You probably have a good idea of where you fall in the evolutionary spectrum. Obviously, your goal is mostly likely to move to the next stage. That’s what this book is really about. We’ll explore what it takes to evolve your data-driven decision making to the next level. In this book, we’ll focus primarily on sales and marketing. In other books, we’ll cover topics like operations, talent acquisition and retention, and research and development.
Evolution requires two things: infrastructure and support. Infrastructure comes by way of knowing the data that needs to be collected and how it is to be analyzed. Support comes by way of having the right people pushing the organization along to do things slightly differently than before.
It’s important to note that in almost every survey of companies going through a process to become a more data-driven organization, the number of key driver of success is executive support. Without it, it’s almost impossible. With it, things tend to fall into place as long as the infrastructure is available.
As we go through the elements of a data-driven sales and marketing function, we can outline the infrastructure. We can even point out where executive sponsorship can influence the process, but getting and keeping the executive sponsorship is your responsibility. And it’s a critical one.
It’s important to note that even if you’ve evolved beyond stage 0, it may still be important to read the sections for the earlier stages. Who knows? You may learn a thing or two that could help along the way. Each of the stages in intended to build on itself, so it’s not completely irrelevant to where you want to be.
Source by Frank Bria
source http://bitcoinswiz.com/the-evolution-of-the-data-driven-company/
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