Data is the key to unlocking the future success of your company. Impressive new developments in data analysis technology now enable us to capture current and past data and display it visually on intuitive dashboards, analyze that data in order to make predictions about what will happen in the future, and manipulate variables to determine alternative scenarios so we can make the best business decisions.
Data is the magic behind AI, machine learning, IoT, and other advanced technology hitting the market. It’s an essentially powerful part of your business.
On the front end of any data analysis effort, though, must lie a robust business intelligence solution. Business intelligence tools examine trends and insights based on what has happened in the past and what is currently happening. They are descriptive rather than prescriptive.
Ultimately, the best BI tools support better decision making for your business by doing things like:
* Pinpointing delays in processes
* Identifying variations in output or outcomes
* Determining which processes most frequently experience delays
* Showing which modes of transportation are most efficient
* Monitoring performance of equipment and personnel
* Tracking employee behavior such as attendance and productivity
Today’s business intelligence tools are faster and more intuitive than previous generations of software, and they are more accessible for average users. But it still takes careful strategy and planning to pull the most useful insights out of your data.
It can be tempting to rush into a BI solution, but that’s almost always a mistake. Poor data practices, poor strategy, irrelevant metrics, missed requirements, and other tactical errors can sabotage your implementation.
Here are 6 steps you can take to avoid that.
1. Start With Strategy
Deploying your BI solution is the last phase in a multi-step process. Before you think about which solution is right for you, define a strategy that lays out your key objectives, personnel, software, and processes. Defining your strategy first will minimize risk of project failure and improve your odds of successfully implementing the right solution. Your strategy should answer questions like these:
* Who are our key stakeholders (executives, IT personnel, analysts, and end users)?
* Who will be responsible for ownership of BI strategy and operations?
* What information do we need, and why do we need it?
* What business problems are we trying to solve?
* What metrics should we analyze to solve those problems?
* Who will use the data and for what purpose?
* What data do we need to limit access to, and to whom?
* Which insights are most important to our overall business strategy?
2. Analyze the Status Quo
Next, take a look at your current software stack and processes. Before you can design a solution, you need to know what is working and what isn’t. What processes and organizational structures are working for you? Which reports are essential, and which can be scrapped? How can you build current processes into your new strategy? Document what you’re currently doing and note which processes are inaccurate, inefficient, or simply not supplying the necessary information.
The next step is to take a look at where your data is coming from and how it is handled internally. Where is your data stored, how is it accessed, and what governance policies do you have in place to ensure data integrity?
3. Define Requirements
To design a successful business intelligence software proof of concept (more on that later), you need a well-defined requirements list. The problem is that it’s not always easy to know what your future requirements will be. That’s why an agile approach to software development is essential. The software should eventually be able to incorporate requirements such as different kinds of reports and additional data points. Of course, that doesn’t excuse you from performing due diligence on your requirements gathering up front. It just means that your solution needs the flexibility to incorporate new needs as they arise—because the more users that see your system, the more ideas will bubble to the surface.
4. Validate Data
If you don’t trust your data, it won’t inform your decision making process, you won’t see a positive ROI, and you could end up paying for it handsomely in lost revenue and other business costs (an average of $9.7 million every year, according to Gartner). The quality of your data determines the success or failure of your business intelligence solution, which is why it’s so important to establish reliable data management procedures.
The data validation process should include ensuring that all relevant data can be accessed, preventing inaccurate or duplicate data from entering the system, and integrating data silos so that various data points can interact to produce the best results.
5. Identify Success Criteria
How will you know if your BI solution is a success? The metrics that answer that question are your key performance indictors (KPIs). KPIs should center on your overall business strategy and objectives. It’s important to remember, however, that a piece of data being interesting doesn’t necessarily make it a KPI. KPIs should tell you if your business is on track and show you where you need to make changes. You can categorize them by function (management, finance, sales, HR, etc.), by platform (SalesForce, social media, ERP, etc.), or in other ways, but the goal is to make sure each stakeholder has the information they need to make the best decisions in their role.
6. Develop your software concept
As you prepare to create and implement your BI solution, your developer will walk you through several incremental stages to ensure that the tools function as needed. The goal here is to determine whether the software can deliver the right results for each stakeholder by creating limited but functional iterations. For example, these are two common stages your developer will work through with you:
Proof of concept (POC) – The proof of concept seeks to demonstrate how the ideas and goals in your BI strategy can be achieved with specific technologies. It should be evaluated based on the criteria you have already defined, and should be examined by your software developers or IT personnel for viability. It may be shared with a limited number of end users for the purpose of soliciting feedback, but is not intended to be a fully functional product.
Minimum viable product (MVP) – The business intelligence software MVP is intended to deliver a fully functional product, but with a minimum number of features. Think of it as a test drive and use it to gather feedback, establish patterns of use, and evaluate user experience.
By limiting scope up front, you can launch more quickly, and make future additions based on real feedback instead of theoretical ideas. The result is a straighter line from MVP to business impact than if you made a lot of guesses and built unnecessary features up front.
At each stage, the goal is to determine whether your new BI software can integrate with your data to deliver insights surrounding your key metrics. That’s why it is so important to put in the hard work of defining strategy, validating data, and determining success criteria on the front end. If you don’t, you won’t have any valid ways to evaluate the software’s effectiveness.
Don’t fall into the trap of churning out lots of BI solutions quickly in hopes of bypassing the strategic process. Neither should you fall for flashy SaaS offers that promise all the answers in one package.
Successful BI depends on quality over quantity. If you put in the time on the front end to determine what your business and your users actually need, you’ll achieve better results and better decision-making support once the solution is implemented.