3 Keys to Managing Data and Maximizing Business Intelligence

I’m about to share two ideas that could transform your business intelligence (BI).

1.  Not all data is the same.

2.  How you manage data matters.

Not convinced? Hear me out…

First, let’s unpack the idea that not all data is the same.

3 Data Dichotomies

“When collecting and analyzing business intelligence data, analysts most often focus on their organization’s internal data (70%), business systems data (59%), and structured data (58%).” (Source: The Silicon Review)

1. Internal vs. External Data

Internal data is information generated from within the business, covering areas such as operations, maintenance, personnel, and finance. External data comes from the market, including customers and competitors. It’s things like statistics from surveys, questionnaires, research, and customer feedback.

Research has shown that business analysts consider data generated internally to be more valuable. According to one survey, “About 65% of respondents rank internal data as more important than data collected outside the company.”

Both kinds of data are helpful. Internal data helps you run your business and optimize your operations. External data helps you better understand your customer base and the competitive landscape. You need a clear view of both to have truly insightful business intelligence.

2. Structured vs. Unstructured Data

Structured data is considered more traditionally as BI, because it’s quantifiable. It’s easier to put in a database, search, and analyze. Conversely, unstructured data is considered a newer type of data. It’s not pre-defined and is typically text-heavy information, such as that from social networks or customer comments.

As explained by Dean Abbott, Co-founder and Chief Data Scientist of SmarterHQ, “Newer types of data are more difficult to use because that data isn’t in a user-friendly form. All the old-school data is in a structured form, so you can put it in the database, apply algorithms, and get value from it much quicker.”

Too often, business leaders accustomed to data snapshots default to structured data to make decisions. This can be shortsighted. A strong BI plan accounts for unstructured data to leverage the insight found there.

Here’s an example from our business. Each year, we do an account review with our clients to see how the software we have built for them is performing. We use structured data to make sure things are working right technically, and then ask a lot of questions to collect unstructured data about how the solution is working for business. We use both types of data in conjunction with our clients to identify challenges to address and opportunities to embrace. The strategy for our clients going forward is far more valuable because it is built on both structured and unstructured data.

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3. Historical vs. Real-time Data

It’s a real BI dilemma: do you look at the present at the expense of the past, or do you spend so much time on last month’s numbers that you don’t see the data for today?

The answer? Do a mixture of both. Companies often spend significant time using historical data to identify and predict trends. But without real-time data to compare it to, the value of that historical data is limited.

A blog post from ArcherPoint Retail gives a great practical example of the relationship between historical and real-time data…

“If sales are down a certain percent today, you’ll want to be tracking that in real time. But, before you start panicking, look at the historical data to see if this dip is a natural progression in your sales or if it is an anomaly. Isolated events can occur, but often times you may see a clue in your data. If you have the historical data already analyzed, you’ll be able to easily detect how sales should react in the future.”

Despite the value of real-time data analytics, a 2015 article reported that a study of 235 US based firms found that only 25% are embarking on real-time predictive modeling, and another 47% are actively exploring the opportunity. The remaining 28% still sitting undecided on how to proceed.

Real-time data can help you make a pivot when a problem or opportunity comes up. But if you react to every blip on the radar, your business will never have long-term success. You need to balance both kinds of data to make intelligent decisions.


Now, let’s turn the page to explore how managing data matters.

3 Keys to Managing Data Well

“BI and analytics requires an extensive knowledge and understanding of how strategic and other organizational decisions are made, and how this can be improved to support growth and value creation in a company.

Different solutions and data strategies support different decision structures. Strategic analytics/BI solutions can be extremely empowering for employees, and create value by informing decision making if it fits the structure. It can be equally as dangerous if there is a misfit between company strategy and data strategy.” (Source: Professor Derrick McIver via Blue Granite)

1. Start at the end

When deciding how to organize your data, it’s essential to first clarify the objective of BI for your company. What’s its purpose? What do you want it to achieve? The last thing you want to do is create a structure that doesn’t give you the intelligence you need. Start with the end in mind and work back from there to structure your data with the desired results in mind.

2. Customization increases power

BI dashboards are data visualization tools that consolidate and display key performance indicators and other metrics in a single screen. Typically, the dashboard summarizes data at a high level so it can be processed at a glance, while offering the option for you to drill down to deeper levels.

Yet, not all departments in your company need to see the same information, so it’s important to organize your data in a way that allows for customization. “Self-service dashboards,” as they are called, allow users to choose their own KPIs, blend structured and unstructured information themselves, and see different visualization options. Not only does this increase the power of the data itself, but it also takes some of the pressure away from IT departments—which have traditionally been expected to create specific reports on request.

However, there are some concerns to self-service dashboards – which leads you to my last point…

3. Put policies in place

One of the most obvious concerns with self-service dashboards is data quality. If users pull the wrong data, they’re going to get the wrong result. And if they’re using the wrong result to make business decisions, that’s a problem. Likewise, you must be confident that the data sources you’re working with are correct and complete, and that data that is proprietary and/or private remains secured.

These issues are why data governance is so important. Data governance is a set of policies and procedures that protects the integrity of your business’s information. It assigns accountability to ensure data remains secure, accurate and usable.

Equally important is training your employees in how to use BI safely and effectively, to ensure…
* They feel comfortable using a dashboard—because if they don’t, they likely won’t.
* They pull accurate data, in the correct way, to achieve an accurate result.
* They follow rules designed to keep your data secure internally and externally.

One way to solve these problems is by building dashboards based on user workflow and real-world feedback. This will ensure that each employee gets the data he or she needs in a way that actually fits the work they do. Often, when Worthwhile is starting to build a dashboard for a client, we will spend hours shadowing employees and taking notes so we can architect a solution that will actually work in our client’s business on a day-to-day business.

BI can be one of your company’s greatest assets. But setting up a BI system is an investment—and only one that’s worthwhile when you structure and manage it in a way that allows you to maximize the value of your data. Once you understand the types of data and data dichotomies, you can embrace the keys to managing data well.

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