What is Business Analytics?
According to TechTarget, business analytics (BA) is the “practice of iterative, methodical exploration of an organization’s data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision-making.” To use business analytics, you must also use big data.
With BA, you treat your data as an asset and use the insights to gain a competitive advantage. To be successful, you must have quality data and a team of skilled analysts who understand the technology and your business. Your organization must also be committed to using the data to make decisions.
Business analytics can be separated into two main categories: business intelligence (BI) and statistical analysis. BI is a basic approach that looks at historical data to gain more insight into how a department, a team, or a particular staff member has performed over a certain time period. Statistical analysis, on the other hand, is a deeper approach that involves predictive analysis through the use of statistical algorithms to the historical data to make predictions about future performance. It may also refer to using other advanced analytics techniques.
“Business intelligence focuses on collecting data to prepare it for business analytics. Though the two are similar, BI is just the first step. BA is the analysis of data collected that shows why something happened and whether it will happen again.”
What is Big Data?
TechTarget describes big data as “an evolving term that describes a large volume of structured, semi-structured, and unstructured data that has the potential to be mined for information and used in machine learning products and other advanced analytics applications.” The amount of data you have isn’t as important as the analytics that goes along with it. When your company analyzes the massive amounts of data you have on hand, you’re using BA to get insights to make better business decisions and adjust your strategy.
Types of Business Analytics
There are three main types of business analytics, each of which serves a purpose for your organization. Your company may choose to use one or more of these styles depending on your needs, which are determined by the business goal of your data analysis.
This type of analytics tracks key performance indicators (KPIs) to help you see where your business is currently.
Predictive analytics analyzes historic data against statistical methods to determine the likelihood of future outcomes.
Prescriptive analytics uses past performance to provide recommendations about how to handle similar situations should they arise in the future.
Business Analytics Tools
There are several varieties of business analytics tools available, such as:
- Data visualization tools: These tools convert the data into a visual format in an attempt to make it easier for people to understand it and more quickly spot trends, correlations, and patterns for opportunities or problems.
- Self-service analytics platforms: These do not rely on needing a data scientist on staff to make sense of everything. With these, businesses can easily manipulate data to find opportunities without needing to fully understand data technology or statistics.
- Business intelligence reporting software: These data analytics applications are designed to retrieve, analyze, and report data for business intelligence.
- Big data platforms: These are IT solutions that combine the features and capabilities of multiple big data applications within a single solution. With a big data platform, your company can develop, deploy, operate, and manage your company’s data.
Self-service platforms are increasing in demand because people want something that’s easy to use and doesn’t require any kind of special training. Tools like Tableau and Qlix can be installed on a single computer, or on server environments for an enterprise-wide deployment. Once things are running, business analysts, as well as employees with less specialized training can use the platforms to generate reports, web portals and charts to track specific metrics in data sets.
Benefits of Business Analytics for Data-Driven Decision Making
Data-driven decision making allows your company to automate and optimize business processes. The insights your company gleans from the data give you a completive advantage because you are able to:
- Conduct data mining to explore the data to find patterns and relationships
- Test previous decisions with A/B testing, also known as split testing, and multivariate testing.
- Complete quantitative analysis and statistical analysis to explain why you’ve gotten certain results.
With BA, you can also make proactive tactical choices and automate the decision-making process for real-time responses.
Challenges Associated with Business Analytics
As with anything else in business, BA comes with its own set of challenges. With big data comes the potential for privacy invasion, greater risk of spending time and money chasing poorly defined business problems or opportunities, and of course a greater possibility of considering “noise” in the data as an insight. Of course, there are other challenges with developing and implementing a business analytics plan within your organization. They include:
- C-Suite Ownership: Senior leadership must be on board and help create a clear strategy for implementing BA in your organization.
- IT Involvement: IT must be on board to ensure you have the right technology infrastructure and tools you need to handle the data and BA processes.
- Available Production Data vs. Cleansed Modeling Data: Pay attention to technology infrastructure that restricts available data for historical modeling. Know the difference between the historical data for model development, and real-time data in production.
- Change Management: Your organization must be prepared for the changes that BA will bring to your current business operations and technology.
- End User Involvement and Buy-In: Anyone who will be using the BA needs to be involved in adopting in and have some kind of stake in the predictive model.
- Project Management Office (PMO): To be successful, you must have a project management structure in place to implement any predictive models and keep your approach agile.
Developing a strategy for BA and implementing it is not something your company can or should do overnight. Following best practices ensures you will get the deeper levels of insight you’re looking for, allowing you to become more competitive, and ultimately more successful. Though not a complete list, here are some of the best practices you should consider, depending on your organizational needs:
- Know your objective: Why do you want to use it. Define your use case and your goals ahead of time. This will help you choose the best analytics tools for your needs.
- Define criteria: What are your criteria for success? Your criteria for failure? Outlining these ahead of time will help you see whether your implementation is on the right track or not.
- Choose your methodology: When doing so, be sure you know the data along with relevant internal and external factors.
- Validate your models: Use your criteria for success and failure to validate your current model. Make adjustments based on results.
If you want your organization to stay competitive and successful, you must invest time and effort into business analytics. Today’s business cannot reach their full potential without it. When you get all your best practices in place, along with the support of your executives and other staff members, you will see great benefit to data-driven decision making. Remember, it’s not the volume of data you have, it’s the data quality that really matters.
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