Data analysis is a means to achieve an end goal. For most businesses, the end goal looks something like: retaining more customers, retaining valued employees, reducing costs and overheads, or just operating more efficiently in general. More and more businesses are beginning to discover this; and discover the value in their data. Therefore, more and more businesses are beginning to develop “data strategies.”

Most executives and businesses start by finding data analytics tools. For example, a common tool, Tableau is a data visualization tool. However, data visualization is not the same as analytics. There are many tools that are needed to achieve data analysis; but many people don’t know this. And even if you do invest in all these tools, they may not even help you that much in achieving your end goal. Because ultimately, all these tools are just that…tools.

A Tool Is Just That…A Tool: It Does Not Work Itself

Think of a data analytics tools as a sewing kit. A sewing kits helps you fix a torn seam. But A) it does not fix the tear by itself, it needs the manpower to do it and B) if you know nothing about sewing, or how to do it, or what the different pieces in the kit are used for, you are probably not going to fix that tear. If you decide to go online and YouTube “how to sew,” you will spend a lot of time training yourself; figuring out what all you need to buy to fix the tear, what size needle to use, and the technique in general. Then when it comes to actually sewing the tear, since you are not experienced with it, you may hurt yourself, and the sewing job might be pretty bad…And you may have to toss the shirt, or send it into a professional. It probably would have been more worth your while to have just sent it to the dry cleaner or seamstress in the first place. They will sew it right up for you. It saves time, you get a much better result, you don’t experience the anxiety from it all, and it probably saves you money in the end too.

A Tool Is A Front-End Solution, Not an End-To-End Solution

As I had previously explained, a data visualization tool allows you to view the data in a more “human-friendly” way, eg. with charts and graphs. However, visualization is not analysis. There are multiple steps involved in data science, and individual tools cannot do them all…or at least not do them well.

Data Prep: Clean and Sync

To analyze data you must first do a few other things. First you have to prep the data by cleaning and syncing it. You have to gather all of your data from all the disparate sources (such as Excel, Salesforce, CRM software, Google Analytics etc.). While there are daily effective tools for cleaning your data, syncing your data is much more difficult. If you do not sync your data, you will have to repeat the same work each time you add new data to a report. It is nearly impossible to do manually, and the software to do this for you can be costly, especially for small and medium businesses.

Tool Updates

You will also have to constantly update your tools, in order for them to truly be useful for your organization. This is possible to do with an IT team, but it can easily get chaotic and messy. All others must wait for the IT team to complete the updates, without attempts to access data. Otherwise you will end up with a total mess of dashboards and reports; leading to everyone getting different numbers and insights that mean different things, and you’ll never know who has the final numbers.

The Analysis Itself

Finally comes the analysis. Finally, the end result…well, sort of. Analysis involves solving very complex calculations that involve a few different sets of numbers. A proper analysis requires multi-stage formulas that perform a number of calculations, occurring simultaneously. For example, to determine your average number of sales in a month, you need both the sum and the average of all of your sales for that month. Sounds easy, right? Any math whizz can figure that out, so why can’t a computer? This can be done by programs, such as Excel, but again, it involves a great deal of manual labor, and time you don’t have to waste. Furthermore, data visualization will involve extra manual labor.  They will restrict the number of aggregations you can input for each formula; so you must calculate the sum. Then save it. Then calculate the average. Then save it. Then you can calculate them together.

End-To-End Solutions

End-to-end solutions are typically in the form of a service (and even these are rarer than you think, as each step requires a specific skill set). However, there are a few tools out there (eg. Sisense) that are advertised as end-to-end solutions. They may get the job done, but they don’t provide you with the deep insight and competitive advantage you wanted and expected from data analysis; but you don’t want to keep up with your competitors, you want to beat your competitors.

Tools: One-Size-Fits-All

The number one thing that analysis (both analysis-specific and end-to-end) tools lack is context. Tools are built as a one-size-fits-all solution. Tools don’t get to know you, your company, or your industry. They also don’t automatically know what type of information you are looking for, what problems you are trying to tackle, and they don’t understand your people and how they will respond to change. I recently saw an anonymous quote that said something like, “100% of your customers are people. 100% of your employees are people. You can’t understand business, without understanding people.”

Data analytics tools (like humans do with analysis) apply a mechanical or algorithmic process in order to derive insights. But what a data scientist does, that a tool cannot, is determine which process, or model will work best for that specific data, to answer a specific question, and to tackle a specific problem. There is a certain level of human intuition that is needed to conduct an effective analysis. It is for this reason that definitions of data analysis often involve the word “heuristic” in them.

So until computers become humans, they will simply not be able to do everything a human can do. People may be capable of doing less, but they are capable of doing different.