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Your excuses for delaying your company's BI project are putting your job at risk

August 29, 2016

What does this blog post cover?

  1. The most common excuses for delaying your company's BI project
  2. How to break the inertia and get started with BI
  3. Resources to help you plan a Better BI Strategy

We often say in the sales department here at TARGIT that it wasn’t so long ago that a business consisted of just three core assets: people, products, and money. All three were critical to the success of the company. In recent years though, a fourth asset has been added, and its importance is paramount: data. Companies that either ignore their data or don’t utilize it as an asset are failing hard and fast.

When company decision makers don’t have the data they need to make critical decisions, at least one of those remaining core assets suffers in some way. And this leads to a systematic breakdown of everything else important to the business.

Surprisingly, what I’ve found is one of the largest obstacles to data isn’t outdated technologies or disorganized data collection, but employees standing in the way.
BI excuses

You ARE ready

My colleague Kim recently shared a blog post debunking the three most common excuses we hear companies give as to why they’re not ready to start a BI implementation. From lack of processes to messy data warehouses to no data warehouse at all, we’ve pretty much heard it all at this point.

I’ve heard variations of these most common objections from prospects since the very beginning of my career at TARGIT. My team and I speak to employees from companies around the world whose executives are constantly requesting analytical insight and aren’t getting the data they need.

These excuses all lead to a form of paralysis. Companies facing these assumed road blocks don’t even know where to start, so nothing gets done to fix the status quo.

Think big, start small

At TARGIT, we always tell our clients to “think big, start small.” Not just in BI strategy, this also applies to the BI implementation process. The single best way to begin a data warehouse cleanup, for example, is to start a business intelligence project. Only then will you be able to pinpoint precisely what is inaccurate or missing from the data. You can read our steps to BI in this free guide: 3 Stages of Becoming an Analytics-Driven Organization.

BI Journey

Delaying BI isn’t just a roadblock to a fully optimized and transparent business strategy, it’s dangerous. We often speak to managers and other mid-level employees who tell us how often their company executives ask for data, reports, and analyses to help them uncover particular insights. These mid-level employees though, don’t want to invest in BI until their data is totally clean and their processes are perfected. 

Before long, the data or technology will look like less of a road block than you do. When your executive team is asking for better insight and visibility and you can’t provide it, not only are you jeopardizing the success of the company, but you’re jeopardizing the success of your career. How many times can you tell the boss “no” before it’s the last time?

Give company decision makers the information they’re looking for. Go as far as you can and worry about cleaning data and processes later.

Looking at your data from a BI perspective—with an end result in mind—instead of a purely data cleansing perspective fosters an environment of data optimized for analysis. You won’t know what data is “dirty” or processes are imperfect until the person who needs that insight is looking at it. You must give it to the decision-makers to figure it out. And for that you need BI. That’s why business intelligence itself will help prioritize the steps of the BI strategy. 

Start with what you have and deliver what you can to the people who need it. Getting some of the way there is better than not even starting. This is just the beginning of the BI journey. This also makes it clearer which data needs to be focused on for the data cleansing process. Now you can start asking questions about why data is the way it is, correct processes, and begin to dig deeper.

We practice what we preach here at TARGIT. We are constantly cleaning our data, ensuring newly inputted fields are tagged properly, and creating new processes to provide greater insight into our data.

How to get started with BI

All of this directive for action is great in theory, but can still be an overwhelming concept for a company of any size. Here’s how one company recently made it happen:

A manufacturing company of frozen meals in the U.K. has historically compiled data from their production processes by recording everything on paper. Yes, paper. They then punch it into their ERP system by hand. They didn’t have a data warehouse and the process of going back through history and cleaning that all up was an expensive, time-consuming project no one wanted to take on. And they didn’t have to.

Instead, they worked with us to set up attainable goals involving various levels of BI complexity. You can read about how TARGIT helps companies design their BI strategy based off of their goals in this eBook: The Better BI StrategyAction Loop

Then we got to work from a technical perspective. Since the company didn’t already have an existing data warehouse in place, it was significantly more cost effective and flexible for to implement their BI solution using in-memory technology. In-memory databases bypass the data warehouse by pulling data directly from sources such as the ERP or CRM systems or from open source frameworks and data lakes. It then sends that data to the BI server for analyses.

Today when this frozen foods company compiles data from their production processes, they still have a huge pile of paper that is punched into their system by hand. This obviously isn’t a perfect process. In fact, it makes most of our consultants visibly cringe. But getting whatever data they can into TARGIT Decision Suite, however imperfectly, gives them significantly more insight than they had before, and helps them prioritize which data is most important so they know precisely how to best clean up their processes to get it.

The nature of in-memory along with a robust data discovery tool lets them play with data to establish which metrics really are valuable for analyses and which they don’t need to bother with. If they hadn’t just jumped in and started the BI process, they wouldn’t have known where to start with the cleansing process, and would not have been able to see or quantify the value of BI from Day One.

And they get all for a fraction of the cost of a data warehouse by choosing an in-memory solution. Since taking this first step into BI, they’ve been steadily ramping up their strategic process, cleaning their data, and setting new standards for data compilation. Now feel ready to invest in the full enterprise solution with all the bells and whistles.

It’s time to switch your stance from “oh, no” to “a-ha” when you see what even the very beginning of BI will uncover for your company. 


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