In today’s digital age, it is hard to get away from data. Data, seemingly, is everywhere. And the amount of data that exists is growing at a rapid rate. By this year alone, it is expected that roughly 1.7 megabytes of new information will be created every second for every human on Earth. And as we continue to move into a new decade, it’s crucial to note that one of the most evolving technologies is big data technology. While there are several different types of data, big data and data analytics prove to be the most crucial application that businesses and organizations need to consider.
Big data refers to the tremendous volumes of data, both structured and unstructured, that underlie a business on a daily basis. According to Gartner, big data can be defined as “high-volume, and high-velocity or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making and process automation.” This form of data can be used to analyze insights that can lead to better decisions and strategic business moves. It can also refer to a field that treats ways to analyze data and systematically extracts information like data points from data sets that are too large or complex to be dealt with by traditional data-processing application software.
Data analytics is the science of examining raw data to conclude specific information. It involves applying an algorithmic or mechanical process to derive insights and find meaningful correlations between several sets of data. Further, data analytics is often used in several industries to allow companies and organizations to make better decisions, including verifying and disproving existing theories or models. When it comes to improved decision making, data analysts use analytics to allow companies to plan effective marketing campaigns, choose what content to create, and develop products, among others.
And with modern data analytics technology, companies can continuously collect and analyze new data from various sources, such as social media, to update understanding as conditions change. Further, data driven analysis allows for more effective marketing as it lets companies know how campaigns are performing. As a result, better customer service arises as more insights are provided into customers, allowing businesses to tailor customer service to their needs, build strong relationships and provide more personalization. Relevant data reveals information about customers’ communications preferences, concerns, and interests, among others. Finally, when data is generated, it can allow for more efficient operations, as it helps companies streamline processes, save money, and increase their bottom line.
According to Forbes, there are several advantages to incorporating big data and analytics into your day-to-day business practices.
In order to stand out from their competitors, companies can use big data to find out what their customers are looking for, then market directly to them. This establishes a dependable customer base that can also be analyzed for obvious patterns. Big data processes then use those patterns to jumpstart brand loyalty by collecting more data to pinpoint more trends and ways to satisfy customers. An example of this is the famous company Amazon, which uses data to make suggestions for shoppers based on past purchases, other items customers have bough, and browsing behavior, among other factors.
Want to deliver tailored products to your targeted market? Again, big data is the answer. Rather than spending money on advertising campaigns that aren’t successful, big data allows companies to perform a sophisticated analysis of customer trends, which can include monitoring online purchases and observing point-of-sale transactions. By using these insights, companies can create targeted, focused, and successful campaigns that allow them to exceed customer expectations, thereby creating greater brand loyalty.
While big data can easily spot patterns in customer behavior, that capability can also be applied to security. We live in a world with high-risk environments, which requires new types of risk management processes. Big data can improve the efficiency of risk management models, thereby creating smarter strategies to circumvent risk.
Though big data can be instrumental in leading the way in innovating new products, it can also help companies update existing products. Through data collection, companies are able to determine what fits their customer base. Rather than guessing, companies today can track customer feedback, product success, and what their competitors are up to.
Big data allows companies to offer supplier networks, AKA B2B communities, greater insights and precision. Its application lets suppliers use higher levels of contextual intelligence, which is crucial for success. An example of this application lies in supply chain networks, which are looking at data analytics as a disruptive technology by changing the foundation of supplier networks to include high-level collaboration. These types of partnerships let networks apply new knowledge to existing problems or other scenarios.
According to TechRepublic, there are eight key trends for analytics in 2020 that will drive vendors to add new capabilities and expand their offerings. Are you familiar with the following?
More analytics will be driven to real-time environments thanks to the fact that in-memory costs are decreasing. The demand for real-time analytics will require fast central processing units and in-memory processing as companies demand the ability to instantaneously respond to online sales activities, alerts about their production infrastructures, or sudden changes in financial portfolios and markets.
It is challenging to capture different voice intonations and accents with accurate natural language recognition, which is why voice-based applications and analytics have not moved rapidly over the past few years. However, natural language recognition, interpretation and mechanics have greatly improved so that more and more analytics queries can be asked via voice command. This type of technology can be ideal in fast-paced work environments such as logistics and warehouse yards, which require employees to work hands-free. Natural-language processing can also be a good option for executives and managers who want to collect data by using voice command from their mobile devices.
The trusty spreadsheet has long been the tool companies have utilized when organizing their analytics, but these days, it is not enough to keep up with the complexities of modern day analytics queries. With graph analytics, companies can simply and easily see the connections between different data points by linking people, places, times, and things, which can speed time to market for business intelligence.
Analytics apps will gain more traction as businesses and IT departments will begin to look to these apps in the same way they look to traditional transactional apps. IT will develop life-cycle management procedures and policies for analytics, beginning with application development and testing and extending to launch, support, backup, and disaster recovery.
The various pieces of analytics are going to be organized into an integrated whole by corporate IT and data science departments. Even more exciting, there is a possibility of augmenting these rudimentary analytics with machine-generated data queries through artificial intelligence (AI) and machine learning (ML). Both ML and AI “learn” from data analytics repositories by examining repetitive patterns of data, processing, and outcomes, then posing derivative queries from what is learned. Despite some people’s fears, both AI and ML will merely enhance, rather than replace, human creativity in terms of framing unique analytics queries. Since both AI and ML can quickly understand repetitive patterns, they may be able to deliver certain business insights more quickly to market.
Throughout 2019, companies used analytics to gain an understanding of current and historical situations. In 2020, TechRepublic estimates that predictive analytics will assess future economic conditions, risk areas, infrastructure maintenance, investment needs, and climate trends.
According to TechRepublic, “dirty data” is costing the U.S. economy $3.1 trillion a year. Further, data scientists are spending up to eighty percent of their time cleaning and preparing data. With these facts public knowledge, it is no surprise that enterprises want data automation that can eliminate human involvement in these tedious operations. If these processes are automated, it will free up data scientists’ time, as well as make it more productive, and speed time to market for analytics, which can gather properly prepared and approved data sooner.
TechRepublic estimates that in 2020, IoT analytics will more toward a more holistic approach by unifying the streams of IoT analytics. Further, input companies are becoming invested in an integrated IoT grid that more closely reflects actual enterprise options.
Many businesses collect data, but have no clue what to do with it. The amount of data present in your company can seem intimidating, but you can pinpoint its usage by determining your approach. First, consider the business problem you are trying to solve and how your data might fit into it. For example, you might be trying to figure out how to cross-sell a specific product to a specific subset of users. Based on their browsing history, who might be most likely to buy the product you’re trying to promote?
Secondly, accept that merely collecting and holding onto data does not mean you’ll have the solution to your problem. Forbes notes that most organizations have been collecting data for at least a decade or more. Yet, this “dirty data” can be unstructured or messy. To clean it up, you’ll need to put it into a structured format you can use.
Third, if choosing a firm to manage your data, go with one that can do more than just visualize the data. It should be able to model the data to drive insights that will help you solve your business problem. Further, make sure you have a budget and plan in place, as modeling data is not easy or inexpensive.
As businesses continue to expand, big data analytics fully supports this growth. Organizations are now able to more easily access developing technology, which is leading to even more data analysis. As brands continue to have data at their disposal, they can start implementing the appropriate analysis systems to solve their issues.
Stefanini's Analytics and Big Data services help companies find the best way to compile, organize and analyze data. Learn more here.
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