How BI Impacts Your Company's Bottom Line

Like most modern businesses, your company likely collects a mountain of data about customers, market performance, and more. But how are you using that data? Is it just sitting there, or does it play a significant part in your business intelligence (BI) efforts?

Business intelligence analytics is a game-changer that can take your company to the next level of success. Here are a few ways BI can affect your bottom line.

BI for Decision-Making

Business intelligence is one of the most valuable tools you can have in your arsenal. Data is only helpful if you can do something with it. For many companies, it's nothing more than bits and bytes sitting on a hard drive.

But with business intelligence, that data becomes helpful to you at the moment. It can help you see the bigger picture and make crucial decisions that guide your company in the right direction. Whether looking at sales trends or marketing ROI, it makes a difference in critical decision-making.

Employee Empowerment

With the right business intelligence platform, you can empower your employees to become decision-makers on their own. The problem with standard data collection and analysis is that it often requires help from a data scientist.

Analysts work with a small collection of data to answer specific questions. Using that information requires many steps that are usually unavailable to lower-level employees.

But with business intelligence analytics, that data is accessible to the masses. It empowers your employees to take action and make decisions without jumping through hoops or seeking constant approval from high-ups.

Establishing a Corporate Strategy

Intelligence can also help your entire company follow an established corporate strategy. Decision-makers often rely on data to develop goals and take steps to succeed. But that information doesn't always trickle down to every department. A lack of data transparency and access makes it difficult for every team to stay on the same page.

But with business intelligence, aligning your teams is a breeze. It drives accountability, increases productivity, and ensures that every department is working towards a shared goal of boosting the bottom line.

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Why Having Peer Reviews in Your Business is Beneficial

You’ve probably heard about peer reviews in relation to academics and scholarly publications, but you may not have heard about peer reviews in a business. A peer review simply means that one’s peers review a person’s work. In the world of publishing, this method of review is used to substantiate claims so that laypeople can trust that what is published about complex subjects is true.

In business, a peer review helps employers to find out how an employee’s performance rates are based on the interactions the employee has with their peers. Peer review is one of many data collaboration tools that can be used to connect different departments and teams. Using peer review and other data collaboration tools, employers can get a better sense of what is happening on a day-to-day basis among their staff.

How Peer Reviews Work

A peer review is usually conducted by team members who work closely with one another. This is because these are the people most likely to have firsthand knowledge of one another’s performance.

In most cases, a simple anonymous survey can be sent out asking each employee to rate the performance of their peers. These surveys should not name employees directly or insinuate that a particular employee is being targeted for review. Instead, these surveys should be general, but they can ask for specific feedback regarding recommendations to improve performance.

The benefit is that employers who are not able to interact with and monitor daily performance can get a better “on-the-ground” sense of what is going on in an organization. Feedback can also assist in helping employers craft training materials to improve productivity.

The Potential Pitfalls of Peer Reviews

Perhaps the biggest drawback surrounding peer reviews in business is the potential for bias. When you ask a team member to review the performance of their team, they are more likely to provide positive feedback as opposed to negative feedback. This can color their responses unnecessarily and lead to the creation of a false impression of reality.

Likewise, if a particular employee has a personality conflict with another employee, the potential exists for negative feedback to be given simply out of spite. This can once again provide a false image to employers regarding what is going on.

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Understanding the Different Advanced Data Analysis Methods

Data analysis is at the heart of research and progress. This certainly applies to academic settings, but it also applies to commerce of all kinds. Today’s smart businesses are using advanced analytics to engage in things like machine learning to fully utilize Big Data and datasets.

Through the use of connected technologies, modern analytics solutions are more powerful than ever. An advanced data analytic solution that taps into the power of the Internet can derive much more detailed information about customers, commerce trends, and even predictive behaviors that could impact future sales.

If you haven’t looked into advanced data analytic solutions, below are the three main methods to pay attention to:

Real-Time Analytics

Real-time analytics is a commonly used advanced data analytic solution that relies on feedback derived from input as it is entered into a dataset. Using real-time analytics, a system can evaluate changes as they occur, providing you with a competitive edge to make decisions faster and with more certainty.

Predictive Analytics

Predictive analytics is another method of advanced analysis that attempts to predict future behavior based on data that has been entered and changes that have occurred as a result. Predictive analytics is invaluable for modeling potential outcomes when decisions need to be made and it would take too long to create traditional models through manual analysis.

Augmented Analytics

Augmented analytics involves the augmenting of the analysis process using artificial intelligence (AI). This method of advanced data analysis can provide a deeper level of understanding of data that would take a very long time for a human analyst to sift through, making it an important tool to rely on for businesses.

Using augmented analytics, a business owner can identify trends and patterns of behavior among customers and markets, allowing for more intelligent decisions to be made faster and with greater accuracy and confidence. While AI cannot completely replace analysis by a human, it can provide a valuable complement to the process of making business decisions.

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Strategies to Make Better Business Decisions Using Data

Data management and analysis are a big part of modern business. You hear about the importance of data analysis all the time, and many platforms are giving professionals the power to create queries and generate reports without help from IT or data scientists. It's the very definition of self-service analytics.

But exactly how are you supposed to use data to make a business decision? In this blog, we'll talk about a couple of common approaches.

Data-Driven Decision-Making

Here's the strategy you hear about most often. With this approach, the data is the deciding factor of all major decisions. It eliminates any biases and relies solely on hard evidence.

Organizations use data-driven decision-making all the time. Think about A/B testing ads or using demographic data to develop marketing plans.

While impactful in many cases, this approach does have some drawbacks. The biggest is that it ignores the bigger picture and removes some of the human element from the equation. Also, it requires a trove of data to be effective. If you're using only a tiny sample, reports might not accurately represent market conditions.

Data-Informed Decision-Making

With a data-informed strategy, you're still using reports to guide you in the right direction. But instead of relying on it entirely, you're keeping the human element in the mix. For example, you might use the data to identify trends and gauge where the market is going. You may even compare the data to what your competition is doing, taking both factors into account before deciding.

The definition of self-service analytics is allowing business professionals to utilize data and generate reports without in-depth knowledge of data science. Data-informed decision-making is perfect for self-service analytics because it doesn't require a ton of data expertise to use.

The only downside of a data-informed strategy is that biases and stakeholder influence could sway the organization's decision-making. The data isn't the deciding factor like before, so there's plenty of wiggle room for things to go south.

Making the Most Out of Data

Ultimately, there are pros and cons to both of these strategies. However, data-informed is often the go-to. Data should always be the backbone of significant business moves, but having the flexibility to consider other factors can make all the difference.

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Techniques Data Scientists Use for Effective Analysis

Data without analysis is fairly useless. After all, what’s the point of collecting data if no one is going to look it over, analyze its significance and report on its contents?

Data scientists specialize in analyzing data for businesses across a variety of industries. While data science for business often focuses on information technology and its benefits for customer experience management, data analysis is vital for virtually any industry that collects large amounts of information.

Different Techniques for Different Needs

When analyzing data, there are different approaches depending on the desired outcome and purpose. Different approaches and techniques are used because data can be very diverse and may experience drastic changes over time. What was an appropriate analysis technique a year ago may no longer apply, so different techniques are available to help as things progress and change.

Below are a few examples of techniques used in data science for business:

Factor Analysis

Factor data analysis is used to condense large amounts of data into smaller figures by correlating like datasets. This technique is predictive in a way as it attempts to pare down data by comparing variables against one another and eliminating certain data when patterns emerge. Clusters can also provide an idea about where certain patterns are migrating toward or away from during a campaign or fiscal quarter.

Cluster Analysis

Cluster data analysis creates groups, or clusters, of similar and dissimilar data points as they relate to one another. This technique is useful for seeing how differing data points relate to one another, both positively and negatively. In addition, cluster data analysis can provide insight into how data may change over time when points move from one cluster to another.

Time Series Analysis

Speaking of measuring things over time, data scientists have a specific technique to do just that called time series analysis. This technique involves measuring data changes at specific points in time across all variables. Essentially, this provides a snapshot of where data was oriented in the past, where it is now and this may even be able to predict where data may end up in the future.

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