Big data analytics takes that data and then presents it in meaningful ways by utilizing powerful visualizations and dashboards. Well-presented data can give decision-makers and managers the intel they need to, for example, move forward with product launches or scale back their marketing efforts.
This type of software often uses four types of analytics to help generate and uncover insights:
- Prescriptive analytics
- Descriptive analytics
- Predictive analytics
- Diagnostic analytics
It might be hard to believe, but big data analytics can help users reduce costs in their business. Hadoop and cloud-based analytics provide cheap and efficient ways of storing users’ data. The added benefit of having a high-level overview is that you can see under and overperforming facets of your business.
For example, let’s say that your marketing department is bringing in a large number of leads, but your customers are abandoning their carts during the checkout phase. By utilizing big data analytics, you can investigate what factors are contributing to your lost sales and treat them. Maybe your checkout page isn’t well optimized? You could spend additional resources on engineering in order to remedy the problem.
Make decisions faster
With wide availability of data, users are often able to make critical decisions quickly. Less time needs to be spent mulling over small data points that aren’t yet analyzed or compiled.
Optimize business performance
Seeing every facet of your business has significant benefits because it allows users to diagnose pain points or deficiencies, and then treat them. When certain departments aren’t performing or meeting KPIs, users can investigate why. Most big data analytics products will help provide at least some diagnostic information, such as corroborating factors or associated data points.
Data management — often known as data governance — is a critical feature of big data. As regulations such as the General Data Protection Regulation continue to have an impact on the way businesses handle data, controlling the flow of that data is a matter of critical importance. Data quality management usually includes cleaning, harvesting, distribution and contextualizing of the data.
Analyze and predict trends
Predicting trends and analyzing behaviors are among the most coveted features of big data analytics. Working off of historical data and evidence, big data analytics will then attempt to make projections and predictions while also accounting for a number of additional factors that can influence outcomes. Factors such as seasonality, price fluctuations, discrepancies, consumer behavior, brand interaction and more are usually accounted for in making predictions.
As an added benefit, predictions help leaders prepare for the future. Let’s say a certain product such as plastic Easter eggs historically sell well in the spring, according to historical data. Managers can then make sure they have plenty of them in stock for the seasonal boom.
Sharing insights with others in your organization is a critical function of any analytics suite, not just big data analytics. Almost always, these data discoveries are communicated through the use of dashboards, reports and visualizations — each of which serves their own unique purposes.
Dashboards are live-updating, interactive windows into raw data. Dashboards are often highly tailored for specific use-cases, such as marketing, sales or management. While reports are generated and then considered “complete,” a dashboard is technically never complete. It shows information in real-time, utilizing visualizations to meaningfully convey information. Dashboards can more often than not be manipulated and explored by the user.
On the other hand, reports are static pieces of content that compile designated information and then deliver it using figures, visualizations or both. Often times, reports are generated at the end of a workday or any set period of time and serve as benchmarks for performance.
Visualizations refer to the vital, illustrative components that are often utilized by dashboards and reports. Visualizations help tell data’s story by communicating in efficient and meaningful ways. Some visualization tools include:
- Word clouds
How Can Big Data Improve Business Performance?
Big data tools are going to pull massive quantities of data across the entire spectrum of your business. When decision-makers and managers can see the breadth and scope of their enterprise — and how it’s performing — they can take steps to capitalize on wins and optimize their losses.
Am I Ready For Big Data?
There are a couple of key factors that play into whether or not your business is ready to start taking advantage of big data analytics. Consider:
- Your organization’s goals in utilizing data
- Pain points and weaknesses of your business
- Unexplained and erratic customer behavior
- Your organization’s need to track certain facets of business
- Lack of progress
How Do I Select a Big Data Analytics Solution?
Picking a big data analytics tool that fits your businesses’ needs is no small task. Just be sure to keep a few essentials in mind when you’re browsing for software.
Think about your enterprise’s needs, first and foremost. Decide what big data analytics features you’re going to need, what you want, and then start shortlisting products. We’ve got curated product pages with features and benefits lists to help make this process a bit easier. We also have a helpful tool called Requirements Hub that can assist users in creating a requirements list for their business.
Next, think about your budget. How much are you willing to spend, and are you willing to go higher for necessary features?
When you feel like you’ve got enough of the necessary information down, it’s time to start an RFP, which is a task in of itself. If you’ve never done an analytics RFP before, head on over to our article, which explains the process in depth.