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What is Data Analytics? How Maner Can Assist.

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Are you in a scenario where you and business leaders ask why are customers drifting away? You take this opportunity to turn to data analytics as your tool. You dig into customer behavior patterns, purchasing histories, and feedback logs and a clear picture emerges.  

It’s not just about products, but the entire customer experience. You and your team orchestrate a seamless redesign, focusing on customer satisfaction, and sales begin to climb. 

But data analytics doesn’t stop there. It’s a continuous journey. Data analysis aims to use data to make decisions and gain insights to improve business management, performance, and identify trends.  

To better understand data analytics, the main steps involved in the process are as follows:  


Step 1: Clearly Define the Business Problem You’re Trying to Solve. 

It’s crucial to have a clear understanding of the problem you’re facing before digging into the data. Without proper data preparation, even the most sophisticated analysis techniques may yield misleading or inconclusive results. Investing time and effort into this stage at the very beginning greatly enhances the overall integrity of the analysis. 

Be certain to ask yourself questions like:  

  • “Why are we investing in this data project?” 
  • “What do we need to learn?” 
  • “Are we looking to gain any specific insights?”  
  • “What potential barriers might we come across with our data?” 
  • “What does success look like for us?”  

Clarifying and discerning the true intent of the analysis helps ensure actionable results. Investing in this step makes it less likely your organization will have to do extra work (or rework) to gain the insights you need when the project wraps.  


Step 2: Collect and Clean Data. 

After clearly defining the problem, the next step involves collecting all relevant data related to the problem or potential solution. Information can come from inside systems like accounting or CRM databases, or from outside sources like open-source databases or websites. 

Frequently, people use a combination of sources. While this collection process can seem overwhelming, you have a few different options when it comes to how the data is structured and formatted. It can be placed into databases or spreadsheets, or left as unstructured, including text, images, and videos.  

cost segregation meeting

In its raw form, data is rarely perfect, and will likely require cleaning and preparation. This involves removing errors and duplicates and consistently formatting the data, such as an address or phone number.  

It’s key to understand how the data should be provided to the team that’s managing the analysis before pulling and formatting. This should be a conversation during the initial project scope and kickoff.  

After cleaning and preparation is complete, the data is ready for analysis! 


Step 3: Analyze the Data.  

There are various methodologies that make up the analysis step. Below are four types of data analysis methods:  

Descriptive Analytics: 

This data method describes what happened in the past, using historical data to gain insights into past business performance and trends. Descriptive analytics comprises key performance indicators (KPIs) that measure financial performance, customer engagement, and inventory management. 

Descriptive analytics can help organizations find what’s working well and what needs improvement. By studying previous data, businesses can learn important information about their operations and customer behavior. This knowledge can help leaders make better decisions and develop smarter business strategies. 

By analyzing historical sales data, an organization can identify patterns and trends in product demand throughout different seasons and promotional periods. These insights will allow a business to optimize its inventory levels, ensuring that popular products are adequately stocked while minimizing excess stock for slower-moving items. The business not only can improve its overall supply chain but also enhance customer satisfaction. 

Diagnostic Analytics:  

The goal of Diagnostic analytics is to understand the reasons behind events. It can help clarify the primary cause of an outcome and allows for reproductions or adjustments to improve business results. Diagnostic analytics can assist in understanding sales drops or budget differences. 

By delving into the underlying factors behind why something happened, organizations can gain valuable insights into the factors influencing their business performance. This deeper understanding enables informed decision-making and targeted interventions to address issues effectively.  

In a retail business, a diagnostic analysis proves invaluable when analyzing a sudden drop in sales for a particular product line. By leveraging customer demographics and potential external factors like marketing campaigns and economic trends, businesses can uncover the root causes of the sales decline.  

Predictive Analytics: 

Analyzing historical data and algorithms can enable forecasting of future events, facilitating proactive decision-making and anticipation of changes before they’re needed. Predictive analytics are especially applicable in sales forecasting, customer churn prediction, and even fraud detection. 

Companies use predictive analytics to improve inventory management, lowering expenses and ensuring products are accessible when and where they’re required. Additionally, healthcare institutions utilize this technique to predict patient outcomes and allocate resources effectively, enhancing patient care and hospital efficiency.  

Prescriptive Analytics: 

An advanced level of analysis, prescriptive analytics provides recommendations on actions to achieve specific goals. The focus lies in improving resources and processes, which organizations can utilize for pricing, marketing, and saving energy. 

Prescriptive Analytics goes beyond data analysis, offering insights, recommendations, and specific actions to reach clear goals. By utilizing complex algorithms and modeling techniques, prescriptive analytics assists businesses in making optimal decisions.  

This type of analytics plays a pivotal role in optimizing online advertising campaigns with businesses. Imagine an online retailer seeking to enhance their marketing strategy for a new product launch. By using prescriptive analytics, the retailer can leverage historical customer data, market trends, and competitor analyses to not only identify the most promising target audience segments but also to recommend the allocation of their advertising budget across various platforms and channels. 


Step 4: Visualize the Data and Make it Understandable. 

After the analysis is complete, presenting the information in a visually appealing and easily understandable manner is essential to making the data actionable. This typically involves using charts, graphs, and dashboards to convey results effectively, rather than presenting raw numerical data.  

Data visualization can also reveal additional trends and outliers in the data.  


Step 5: Share Your Findings!  

Finally, sharing the results is necessary to enable making informed decisions, optimizing processes, and enhancing business performance. After information has been shared with key stakeholders, it regularly leads to additional questions, dialog, restarting the data analytics loop, and further improving decisions.   

Data analytics is a powerful tool that can significantly boost a business’s growth and competitiveness. It helps business owners understand their challenges at a deeper level and make decisions based on actual data instead of intuition. With the ever-increasing amount of data available, data analytics has become a vital component of any business plan.  


Are you ready to leverage data analytics to enhance your business operations? We can help.  

Our experts know the latest industry trends and best practices, so your data-driven decisions are informed and strategic. From data collection and cleansing to advanced analysis and visualization, we tailor our approach to fit your unique business needs. When you partner with Maner Costerisan, you get a team that is dedicated to using data to help you succeed. Let us empower you to unlock new insights and drive growth in today’s competitive landscape. 

The consulting team at Maner Costerisan is here to guide you through the data analytics process and elevate your business performance. Whether you need help walking through the process or support to execute it, we’re here to lend a hand.  

Contact Jessica Droste, CPA, Manager, at or to learn more. 

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