The Importance of Streamlining Data Analytics

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Tech Insights for ProfessionalsThe latest thought leadership for IT pros

Tuesday, July 28, 2020

In most companies, information technology teams spend a large amount of their time organizing data and analytics. Your IT team could be far more effective if they didn't need to spend large portions of their workday cleaning and organizing statistical information.

Article 4 Minutes
The Importance of Streamlining Data Analytics

For many businesses, the information flow can be overwhelming, and the idea of streamlining this process may be daunting. Automating these aspects of your organization will not only simplify things and save you time but also give you a competitive edge. According to Harvard Business Review, the number of companies automating data and analytics (D&A) is growing; this year, global business investments in D&A will surpass $200 billion.  

When businesses fail to maximize what's at their disposal, they place their company at a competitive disadvantage. In an economy where immediate customer satisfaction is not only demanded but also expected, the instantaneous insights that this process could provide make a huge difference between success and failure.

Automating these systems allows a company to have almost immediate insights into important information. They can analyze results in an efficient manner and appropriately adapt their business practices to changing market circumstances and customer demands. This gives them an advantage over competitors and provides their customers with a more favorable experience.

Harvard Business Review reports that D&A is the most in-demand technology skill today, but nearly 40% of IT managers report that their company has critical shortfalls in this area. Less than 25% of organizations believe that their D&A capacity has the ability to optimize business outcomes.

Streamlining with automation

When you integrate sources, you can significantly reduce the time spent compiling statistics and ensure the results are more accurate. By analyzing the information collected through automation, more time can be spent developing effective strategies that improve interactions with customers. According to a survey of international businesses conducted by Statista, 71% of respondents reported that D&A was an integral element of all of their business and financial decisions.

In addition, the cost of the time your employees spend analyzing this information is far more expensive than the cost of automation.  Automated analysis can be performed quickly while eliminating errors and presenting more accurate results. This allows the humans involved to be more effective, improving the quality of their work and freeing them to concentrate on developing real-world scenarios and strategies.

Leveraging the power of automation to simplify your process and get more from your data can be done 4 steps:

Step 1: Identify the problems

The first step is to identify where the problems lie in your process. For many companies, slow and overly complex information preparation leads to problems that can be easily identified. This includes multiple applications that store different statistical information for the same entities. Another issue may be poor or "dirty" data that can negatively impact analytics reports or lead to inferior mapping.

Step 2: Use a reliable management system

Quality sourcing is an important yet often overlooked step in the process. Immediately proceeding to "clean" your systems may simply cause you a lot of needless extra work. A poor information management system will lead to frustration, inferior quality, and formatting problems.

With an effective integration platform, you will have a permanent and easily accessible method for processing. Integration is an essential part of not only curating and updating sources but also allowing that information to be analyzed effectively. This time-saving process allows you to focus on business growth and expanding core services.

Step 3: Choose the proper platform

Now that you have selected reliable sources, you next need to develop the proper integration platform. The integration platform is key to correlating your client information with your analytics engine and plays an important role in the outcome of the project.

Without effective integration, your efforts so far run the risk of being wasted. The ability to unify analytics, data science and business process automation is critical and only achievable through an Analytic Process Automation (APA) platform. Using an APA platform, like Alteryx, will enable you to easily share data, automate tedious tasks and complex process, and turn data into results by bringing your data, process and people together in one place.

Step 4: Stay current on evolving datasets

Whether your company's data is processed in batches or in real-time, there's very little opportunity for a manual cleansing and standardizing of large volumes of information, even though clients may automatically expect it.

By proactively monitoring quality and resolving issues before you begin the automation process, you can prevent "dirty" information from corrupting your project. This requires you to create validation criteria that will assess each new record as it's introduced into your systems.

Too much monitoring, however, can result in unnecessarily complicated reports, making it difficult for analysts to interpret this information or for your shareholders to take action based on what you present. Lax monitoring practices can lead to oversights and costly errors, therefore it's important to maintain a balance and find the right monitoring combination.

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