Modern businesses are generating enormous data regularly that is required at many steps in decision making. Whether it is about determining the mood of the customers or the trends in the markets, data analytics is the key to evaluating and understanding crucial insights from the figures. The value of data can be understood from the fact that data-driven companies are 58% more likely to overachieve the revenue goals when compared to the ones that don’t focus on data.
Many businesses may have no previous experience in managing data, and this calls for dedicated data analytics solutions from third-party vendors. While the data-driven culture is important business intelligence trends of 2021, it is important for the companies to know the basics of data analytics. Let us know all about data analytics in detail.
Data analytics:
It is a self-explanatory term that helps businesses analyze all types of data- structured, unstructured, real-time, qualitative, historical, etc. The patterns thus observed are used in generating insights that help businesses digitally transform entire operations. Many businesses are already using manufacturing data analytics to get actionable insights that are improving their efficiencies while minimizing costs. These can be further used to predict future demand, offer desired products, and improve the overall customer experience.
Must-to-have Features in Data analytics:
The important features of any data analytics system are:
Data preparation: It must include a self-service data preparation that reduces manual input on incoming data. Hence, it helps solve the issues of raw incoming data from multiple sources.
Location analytics: Retail data analytics are of no use when there are no details of the customer’s location. Hence, the second-most important feature in data analytics is to add the geospatial and location insights to spot the unknown data trends.
Machine learning: Human efforts can be eliminated by using machine learning that can task the computers to learn patterns and find insights using dedicated algorithms. New additions like natural language processing, augmented analytics, and image analytics can be looked upon.
Business intelligence: The results from data analytics are a powerful source of information for business leaders to drive changes. Hence, any data analytics system must include reporting and business intelligence tools.
Predictive analysis: Data analytics must analyze the historical data to predict future trends. It reduces the burden on data scientists as software can predict accurate trends.
Streaming analysis: Incoming data from the IoT streaming devices must not get ignored by any data analytics system.
Data visualization: Last but not least is the data visualization that helps all decision-makers understand the results from data analytics quickly.
How is data analytics used?
Many times businesses are skeptical about using data analytics. The quick steps to use data analytics helps understand the right process to implement it using in-house teams or to take the help of the professional service providers.
Once businesses are confirmed of the need for dedicated data analytics in their operations, it can be achieved in the following steps.
Understanding the need of the businesses and key problems that need to be resolved.
Collecting the data from different sources.
Preparing the data for detailed analysis.
Generating actionable insights from the data.
Deploying the analytical models.
Monitoring the models for optimizing the results.
Advantages of Data analytics:
The main advantages of data analytics for any business include:
Analyzing high-volumes of data: All incoming data may not be useful, and hence data analytics analyzes every incoming data and provide actionable insights accordingly. It helps find the anomalies, if any, and further filter, summarize and offer real-time solutions to the possible issues.
Using intelligence to drive data: The results from the data analytics can be automated using intelligence tools. The closed-loop solutions, including the continuous feedback model, use the best intelligence to ensure effective results from the data analytics.
Simplifying and collaborating operations: It helps simplify the complex data management and ensures scalability using advanced tools like Hadoop, Spark, etc. The simple collaboration with data science, business line, and IT teams can significantly improve business efficiency.
Quick and correct response: Data analytics can improve the response time for multiple decisions. The use of microservices and server-less applications confirms that the decisions implemented quickly are correct to the best use of the business.
Effectively operationalize analytics: While every company understands that data is important, very few notice the operational challenges of data management. Data analytics works in the right direction to monitor, manage, and build trust in the results from the data.
What are the main uses of data analytics?
After going through the top benefits of using data analytics, you’re still thinking about whether it is the right technology for you or not, have a quick look at its main uses. If any of the below function in your organization needs assistance, it is the perfect time to implement data analytics in your business process:
Managing customer data from different sources
Detecting frauds and using it in risk management
Performing detailed market research to know all about the latest trends
Identifying any anomalies in data
Analyzing the overall business operations
Personalizing and customizing entire services
Possible issues in Data analytics:
Just like any other technology, data analytics comes with its set of limitations.
Problem 1: Not all data in the business require tools and techniques for accessing insights. Further, the amount of data entering the systems remains a challenge that further puts stress on the data analysts.
Resolution: This problem can be resolved by going for the data sources that offer meaningful and real-time data only. It can be achieved by having a detailed look at the different data sources and finding the necessary or top-rankers according to the aim of the data analytics. The volume of data automatically reduces when the company is betting on the quality over the quantity.
Problem 2: Many times, it is observed that data analytics soon start losing the support of the organizational teams due to multiple reasons. Lack of data management, submissions on time, etc., may cause the entire process to be ineffective for the organizational goals.
Resolution: This problem can be resolved by starting to train the employees about the value of data analytics and then implementing it in the departments. This makes the existing employees familiar with the goals and hence confirms them for their active participation. Hence, team and data analytics comes on the same page to deliver customer satisfaction and high levels of productivity.
Problem 3: Many startups and small businesses worry about investing in data analytics and associated technologies. While it is all about budgetary constraints, there may be a shortage of skills that may call for the dedicated outsourcing that further attracts finances.
Resolution: This problem can be resolved by securing a budget separately for big data analytics. The return from data analytics, when drawn against the investments, makes it easy for the stakeholders to grant necessary approvals. The second associated issue of shortage of skills can be solved in two ways. The internal data analytics team can go for the detailed training, or the entire process can be outsourced to third-party vendors. There are multiple vendors that offer data analytics services to global clients at highly competitive pricing.
Wrapping Up:
To wrap up, business data analytics is the need of any modern business operating in highly competitive environments. The popularity of data analytics can be estimated from the figures that expect the global expenditure on big data analytics to cross 274.3USD billion by 2022. The world has already accepted its importance, and technological advancements like artificial intelligence, cloud storage, etc., are driving the benefits of big data. The dedicated Chief Data Officers or CDOs are the next change agents when it comes to improving business revenue, increasing customer satisfaction levels, and minimizing costs.
