Why is Big Data Analytics Important?

The concept of big data has been around for years, most organizations now understand that if they capture all the data that streams into their businesses ideally in real time, they can apply analytics and get significant value from it. This is particularly true when using sophisticated techniques like artificial intelligence. But even in the 1950s, decades before anyone uttered the term “big data”, businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.

Some of the best benefits of big data analytics are speed and efficiency. Just a few years ago, businesses gathered information, ran analytics, and unearthed information that could be used for future decisions. Today, businesses can collect data in real time and analyze big data to make immediate, better-informed decisions. The ability to work faster and stay agile gives organizations a competitive edge they did not have before.

Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. Businesses that use big data with advanced analytics gain value in many ways, such as:

1.?Reducing cost. Big data technologies like cloud-based analytics can significantly reduce costs when it comes to storing large amounts of data (for example, a data lake). Plus, big data analytics helps organizations find more efficient ways of doing business.

2.?Making faster, better decisions. The speed of in-memory analytics combined with the ability to analyze new sources of data, such as streaming data from IoT – helps businesses analyze information immediately and make fast, informed decisions.

3.??Developing and marketing new products and services. Being able to gauge customer needs and customer satisfaction through analytics empowers businesses to give customers what they want, when they want it. With big data analytics, more companies have an opportunity to develop innovative new products to meet customer’s changing needs. Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it is important to understand the process, an underlying framework is vital for producing results that stand up to scrutiny.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’. Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking, “What business problem am I trying to solve?”. While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as, “Why are we losing customers?” It is possible, though, that this does not get to the core of the problem, a data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.

2. Step two: Collecting the data

Once you have established your objective, you will need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numerical) data, for example, sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data.

3. Step three: Cleaning the data

Once you have collected your data, the next step is to get it ready for analysis. This means cleaning, or “scrubbing” it, and is crucial in making sure that you are working with high-quality data. Key data cleaning tasks include:

• ?Removing major errors, duplicates, and outliers. All of which are inevitable problems when aggregating data from numerous sources.
• ???Removing unwanted data points. In other words, extracting irrelevant observations that have no bearing on your intended analysis.
• ??Bringing structure to your data. For example, fixing typos or layout issues, which will help you map and manipulate your data more easily.
• ?Filling in major gaps. As you are tidying up, you might notice that important data are missing. Once you have identified gaps, you can go about filling them.

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results.

?4. Step four: Analyzing the data

Finally, you have cleaned your data. Now comes the fun bit, analyzing it. The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you are hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

• Descriptive analysis. Identifies what has already happened. It is a common first step that companies carry out before proceeding with deeper explorations. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.
• ??Diagnostic analysis. Focuses on understanding why something has happened. It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. For instance, it could help the company draw correlations between the issue of struggling to gain repeat business and factors that might be causing it (for example, project costs, speed of delivery, customer sector, etc.)
• ??Predictive analysis. Allows you to identify future trends based on historical data. In business, predictive analysis is commonly used to forecast future growth. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they will hike up customer insurance premiums for those groups.
• ??Prescriptive analysis. Allows you to make recommendations for the future. This is the final step in the analytics part of the process. It is also the most complex. This is because it incorporates aspects of all the other analyses we have described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.

5. Step five: Sharing your results

You have finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with your organization’s stakeholders. This is more complex than simply sharing the raw results of your work, it involves interpreting the outcomes, and presenting them in a manner that is digestible for all types of audiences. Since you will often present information to decision-makers, it is very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That is why it is very important to provide all the evidence that you have gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts.

?So, get creative with the steps in the data analysis process, and see what tools you can find. Provided you stick to the core principles we have described, you can create a tailored technique that works for you and the organization.

Article written by Mr. Darshan Gunasekaran, Senior Consultant & Big Data Trainer for AntsBees Sdn. Bhd