3 Must-Know Techniques To Get the Most From Your BI Investment

A decade ago, if you'd asked a typical business manager about their most pressing challenge, they’d probably have told you it was a lack of information. Many companies were essentially operating on instinct because they lacked strong insights into how their business operated.

Nowadays, we face the opposite problem. Cloud service applications throw mountains of data at even small businesses. From your virtual server farm to your email marketing campaign's success, and even to all that inventory you've got sitting in your warehouse, numbers are flowing into your business from all angles. Fortunately, accessible business intelligence (BI) tools have the potential to let even small to midsized businesses (SMBs) parse that data into that unicorn of analytical success, the actionable insight. The difficulty is getting your arms around the numbers.

That’s where business analysis comes in. As we’ll explain in this article, business analysis techniques aren't the colorful dashboards your BI tool will eventually let you build. They're what lets you take your raw data and use it to build a map that leads directly to real-world action. The BI tool is the pack mule that helps get you there.

Many smaller business managers shy away from BI tools because they believe them too difficult to use, but actually, they're not so hard once you know what you want them to tell you. The techniques below will let you understand what those questions need to be and how you'll want them answered.

What Makes Analysis Important

Business analysis needs to begin before you ever boot up your BI tool. A BI service on its own isn't worth much. To benefit from one, you need to empower it to tell you, your managers, and your employees the information they need to know. Data that will let them do their jobs better with a corresponding benefit to the rest of the business.

That should include data they need regularly and it should also make it easier to get at than whatever method they were using to find it before. The cherry on top should be the ability to add new insight. Ideas they might not have had before that hopefully make their jobs easier and more effective. That's good for long-term and repeatable business problems, but it can also be great for short-term challenges.

A great example is the pandemic. COVID-19 is forcing firms to shift everything from operations to financial strategies so everything works in the new normal. That means new insight into current data is now critical. According to a survey conducted by the Freshbooks Communications and Research team, only 21% of businesses said they have enough staff or resources to meet the challenge posed by the pandemic.

While they're both powerful platforms, neither Tableau nor Microsoft Power BI can manage that resource reallocation for you. Business analysis is what you'll need to do before so you can point those tools at the precise problems you need to fix.

Three Top BI Techniques

It might look strange to beginners, but most of the popular BI techniques are referred to by an acronym. These generally abbreviate the steps that the technique uses to perform its function. The three described below are certainly not the only techniques available but they can all be applied to businesses of any size, so they're a good place to start.  

1. SWOT

SWOT analysis is probably the best-known technique, which is why it's first on our list. SWOT stands for Strength, Weakness, Opportunities, and Threats. Because it's so broadly applicable, it's arguably the most popular technique in use today. That's because SWOT analysis can be – and should be – performed across your entire organization, not just one department or workload. 

Doing this reveals a lot about how different teams see the position of your company and the opportunities available to you. By pulling together these responses, you'll be able to build a more holistic picture of how your business works and how it's doing.

An example of this might be the attitude of your team members to new technologies. Depending on your market sector, it could be that your marketing teams are very excited by the Internet of Things, or that your customer service teams see an opportunity in AI-driven chatbots. Conversely, your IT teams might regard precisely those technologies as the biggest threat you face. Applying SWOT would prioritize marketing or help desk needs against your IT mission. It would also cover exactly how to make those decisions by providing data relevant to that decision.

That could cover things like how your customers feel about AI chatbots from a survey you've given them or from responses your help desk technicians have entered into their customer relationship management (CRM) or ticket management applications. That data would then be correlated to relevant IT data, like how much an AI chatbot system would cost to install, manage, and troubleshoot, and most importantly how you'd test such a new technology before actually buying into it.

Because it's focused on four well-defined areas, initial questions and the final results of a SWOT analysis are usually shown in a basic four-point grid. For an oversimplified example, let's say you're running a plumbing business and you're looking to expand. Your initial SWOT questions might look like this:

Addressing these questions will hit different parts of the business, including marketing, sales, finance, and operations. Each area will need to address those questions in terms of what information will be needed to get an answer and build a plan to move forward.

SWOT analysis allows you to balance out the competing perspectives and needs of all your teams, and ensure that you're not missing an important part of the picture. A common misconception about SWOT (and several other BI techniques) is that they're "plug and play," meaning you build a set of queries once and then keep running them regularly to chart growth/decline or success/failure over time.

You can use elements of SWOT for this, but the SWOT concept is about how to approach a BI initiative strategically. Your query approach comes after your SWOT process, not during. And you can and should be applying the SWOT process as often as you can to as many aspects of your business as possible, particularly those that keep changing.

For a hands-on and more in-depth understanding of how to use SWOT in the real world check out an online primer like this one from Udemy.

2. MOST

While you can apply SWOT to a specific problem, it's really aimed at a more comprehensive analysis of your whole organization or department. For figuring out smaller or single issues, a better approach is called MOST, which stands for Mission, Objective, Strategy, and Tactics. In some ways, this technique is similar to SWOT, but the difference here is that MOST analysis works more hierarchically.

You'll start with a single problem at the highest level. Then each step beneath that is designed to be more specific. If you went broad with MOST you might define your business by your executive levels. You could then use that hierarchy to gather and measure the broadest metrics available to you. For example, raw sales or market share growth. That data is then used by senior managers to build objectives, meaning the targets they want to hit going forward. Specifically how much they think they can increase sales. Or how much bigger they expect their market share to grow and, very important, precisely how they're going to measure that. Strategies are your plans for achieving these, and tactics are how individual staff members are going to contribute to that.

The last two steps of MOST will be the most applicable when you're designing ongoing metrics and how you'll analyze them with your BI platform. But before you get there, you also need to figure out how you're going to gather that data.

If your objective is to build your community audience by 15% in two quarters, what metrics will you need to know if you succeeded? Definitely overall social media site visitors, but also things like webinar attendees, event responses, or site visits originating from community and email outreach. With all these channels available to you you must establish data gathering methods before jumping into query design since the questions you can ask will be determined by the data you've got.

In the above example, that'd be measures like site visit counters, email delivery, open, and click-thru statistics, and an accurate counter of webinar attendees over multiple events. Once that data has a designated gathering tool and an established path to your BI system you can start figuring out the queries you'll need to track everything.

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MOST can be a big help in both those steps because it's a straightforward and visual way of organizing your needs, available data, and goals so it's all easily digested and understood by the entire team, not just a data scientist.

If you're looking for more of a deep dive into what MOST can do, Texavi, a UK-based social and behavioral startup, does a good job explaining it in this Youtube video on its Texavi AnalystZone channel.

3. Process Design

Both SWOT and especially MOST can be used by any sized business with good results. But for the SMB segment, staying constantly competitive, efficient, and profitable is the biggest challenge. For that, a more direct and hands-on approach to business intelligence might be more effective. That's where process design comes in. The central idea is that some of the tools that are normally used to plan and manage projects can also be applied to ongoing business processes. 

The trick is understanding how your business works. If you're running an e-commerce fulfillment warehouse, you know you're making money from storing your clients' wares, measuring their inventory, fulfilling and shipping their orders, and gathering the data they need to keep their operations running. With those processes in mind, you can probably map out how much money you're spending on those services versus how much they're bringing in. Or can you?

Exactly how is your warehouse being managed? To answer that you need to look at the process that governs a new product's arrival, storage organization, and shipping characteristics. You should also know precisely how orders come in, how they're processed, and how the money and data exchange works. You're drilling for details with process design. The more granular you can get, the better it works.

That means identifying each of these task chains, categorizing them, and mapping out every step in the process. That information lets you adjust the process for new efficiency, better cost/profit numbers, or whatever optimization is most important to you. As such, there's no established set of steps for this analysis method. Each business will dictate its approach. Probably the most popular way is to steal the MOST approach of descending granularity and apply it to your business mechanics.

Start with the broadest definition of what your business does. Break that up into its parts, which will probably be the same as the departments you've organized it into. Meeting with those department heads, their second-tier managers, and even the guys on the front lines is how you'll eventually establish a detailed compendium of exactly how your business works. You want it all, including the smart successes, chronic hurdles, and embarrassing warts. Your first time will be a long and tedious process but the information is gold.

Once you've got the reality it can now be compared against hypothetical questions – asking managers to envision how processes would work “in a perfect world.” You match those responses against what's going on, which then lets you figure out why it's going the way it is and what you need to do to improve. No organization operates perfectly, which means you can run a process design project regularly and always find new insights. For SMBs, that's a fantastic way not only to operate better but also keep senior management, even if it's just a small group of owners or partners, in close touch with every aspect of how their business is working.

Applying a BI tool to this process works best if you're performing process design at regular intervals. A one-time approach really won't benefit much from a tool like Tableau. But then a one-time process design project will have limited success potential anyway. But do one annually, for example, and you can spend a whole year collecting data that's relevant to each process moving your business forward. Visualizing that data every time you kick off a process design session gives you a highly detailed picture of how you're doing in your market and against your competition.

One Size Does Not Fit All

Data collection without an express purpose is a waste of disk space. It's only worth it if you're using it to plan and realize change, whether that's strategic or tactical.

A good business intelligence tool empowers you to use data to make those changes. It's that goal that should also determine which BI tool you eventually use. It can be an expensive investment, so matching the data you've got and the questions you're going to ask against the capabilities of the BI platform should form the baseline of whether any particular tool is right for you.

For any SMB, applying at least one of the techniques described above is the way to do that.