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Achieving insights from data: the key to making data-driven business decisions

Updated: 5 days ago

Three individuals looking at graphs on a tablet.

Data is a resource that can provide valuable insights into various aspects of business operations, customer behavior, and market trends. However, extracting meaningful insights from data can be challenging, especially if you don't know where to start or how to analyze it effectively.  In this blog article, we will explore some tips and best practices for achieving insights from data.


Achieving insights from data is the fifth step in the Business Data and Analytics Journey.  


Formulate a goal and collect the right data:


The first step is to clearly define the research question or issue that your analysis is trying to solve.  You might be trying to improve customer satisfaction, increase sales, or optimize operations.  Once you have a clear understanding of your goals, determine what data you will need that may be used to support your analysis, you need to collect the right data.  The Scalesology blog article: Best Practices when collecting data for analysis, outlines the steps to take to gather the data you need.   


Process the data:


Once you have collected the data you need, your second step will be to process the data by cleaning and preparing the data for analysis.  Data processing is an essential step in any data-driven decision-making process. It involves multiple steps, including data cleansing, data integration, data transformation and application integration to get the data ready for analysis.  The Scalesology blog article: Data Processing: Transforming raw data to useable information, will guide you towards transforming all that data into usable information ready for analysis.    


Centralize the data:


Now that your data is processed and ready for analysis, you might want to consider centralizing the data to allow for ease in conducting and repeating your analysis in the future.  Another benefit of centralizing your data is building data pipelines from your data sources to your data warehouse/data lake.  Data pipelines allow a continuous stream of data to feed your analytic dashboards and predictive models. The third step toward achieving insights from data is aggregating and storing data in a secure way so that the data can be used for analysis.  Scalesology’s blog article, Centralizing data: the key to fast convenient analytic insights describes the benefits of businesses centralizing data.


Analyze the data:


Now you are ready to analyze the data.  An analytics maturity model, as seen in figure 1. below, is a straightforward way to evaluate your data analytic proficiencies. Gartner provides a simple, well-known model defining four types of data analytics: descriptive and diagnostic analytics provide hindsight, whereas predictive and prescriptive analytics enable foresight.   



The Scalesology Blog Article: Data analytics can turn hindsight into foresight, details the difference and value of analytic types as you go from descriptive analytics to prescriptive analytics.  The best value comes in using quality information from the past to positively influence your company’s future; but this path to foresight does increase complexity of the analytics you will need to conduct.  Most businesses easily achieve their return on investment as they achieve predictive and prescriptive analytic insights.


Achieving insights from data:


So now we are ready to achieve insights from the data.  One of the key benefits of data analysis is that it allows users to identify patterns, trends, and relationships within the data that may not be immediately apparent from looking at the raw data alone. For example, by analyzing customer purchase data, a business can identify which products are most popular among different demographics and use this information to make more informed marketing decisions.


Insights from data can also be used to identify outliers or anomalies in the data, which can help users understand the underlying causes of certain phenomena. For example, by analyzing financial data, an organization can identify unusual patterns in revenue or expenses that may indicate fraud or other types of financial irregularities.


In addition to these common applications, data analysis can also be used to address more complex problems, such as predicting future trends or identifying the most effective marketing strategies. For example, by analyzing historical sales data, a business can use machine learning algorithms to predict future sales and adjust its inventory levels accordingly.


Business leaders can achieve insights from data in a variety of ways to improve their operations and decision-making processes. Here are some specific examples that may apply to your organization:


  1. Customer Segmentation: By analyzing customer purchase data, businesses can identify which products are most popular among different demographics and use this information to make more informed marketing decisions. For example, a clothing retailer might find that younger customers prefer trendy clothes, while older customers prefer classic styles. This information could be used to target specific marketing campaigns at the appropriate customer segments.

  2. Fraud Detection: By analyzing financial data, businesses can identify unusual patterns in revenue or expenses that may indicate fraud or other types of financial irregularities. For example, a bank might find that a particular account has an unusually high number of transactions in a short period of time, which could be a sign of fraudulent activity.

  3. Inventory Management: By analyzing sales data, businesses can identify which products are selling well and adjust their inventory levels accordingly. For example, a retailer might find that a particular product is selling out quickly, so they would need to order more of it to meet customer demand.

  4. Supply Chain Optimization: By analyzing data from the supply chain, businesses can identify bottlenecks or inefficiencies in the process and make improvements to reduce costs and improve delivery times. For example, a manufacturer might find that a particular component is taking longer to produce than expected, so they could investigate the production process and make changes to improve efficiency.

  5. Predictive Maintenance: By analyzing data from machinery or equipment, businesses can identify patterns that indicate when maintenance is needed, which can help prevent downtime and reduce repair costs. For example, a manufacturer might find that a particular machine is showing signs of wear and tear, so they could schedule maintenance before it breaks down completely.

  6. Pricing Strategy: By analyzing data on customer behavior and market trends, businesses can determine the optimal pricing strategy for their products or services. For example, a retailer might find that customers are more likely to purchase a product at a certain price point, so they could adjust their prices accordingly to maximize sales.

  7. Product Development: By analyzing data on customer needs and preferences, businesses can identify areas where they can improve their products or services. For example, a software company might find that customers are requesting more features related to collaboration, so they could add these features to their product to better meet customer needs.

  8. Marketing Campaigns: By analyzing data on customer behavior and engagement with previous marketing campaigns, businesses can determine the most effective marketing strategies for reaching their target audience. For example, a social media company might find that customers are more likely to engage with videos than text-based posts, so they could adjust their advertising strategy to include more video content.

  9. Customer Service: By analyzing data on customer complaints and feedback, businesses can identify areas where they need to improve their customer service processes. For example, a hotel might find that customers are frequently complaining about long wait times for check-in or check-out, so they could hire additional staff to reduce these wait times.

  10. Talent Management: By analyzing data on employee performance and engagement, businesses can identify areas where they need to improve their talent management processes. For example, a company might find that employees who receive regular feedback and coaching are more likely to be engaged and productive, so they could implement a mentorship program to support employee development.

 

Achieving insights from data is a powerful process that allows businesses to make informed decisions based on data-driven evidence.  Businesses can gain a competitive advantage and improve their overall revenue and operational efficiency.  What are you doing to achieve insights from data in your organization? 

 

Are you ready to jump in and start your business data and analytics journey towards deploying data insights into action? We are here to help.  Scalesology will work together with you and develop a comprehensive data analytics strategy so you can make informed business decisions.  Contact us today, it is time to scale your business with the right data insights and technology. 

 

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