The number of industries that use data analytics has definitely grown bigger. Global sales for big data analytics (BDA) software and solutions are expected to hit over $189 billion by the end of 2019, according to the 2018-2022 BDA market forecast by the International Data Corporation. That figure is predicted to reach $274.3 billion when 2022 ends.
It’s safe to say the list of data analytics tools will grow longer as the years come in.
Many companies and organizations are now gearing for total digital transformation, the main driving factor of the increased BDA-related spending. It is difficult to manage and deploy big data technologies on a conventional, on-premise environment. That means the bulk of BDA software and solutions developed and rolled out for commercial and general use will be deployed from the cloud.
There is no denying how thorough data analysis with the use of big data software is helpful in discovering extremely helpful and actionable insights as well as unearthing hidden opportunities for improving business performance. From predicting sales trends and customer shopping behavior to determining potential issues and challenges that can impact the company, data analytics provided organizations and businesses with the information, insights, and recommendations in meeting and navigating the future.
The following are four sectors that benefited most from data analytics software solutions and how they make the most out of their data.
1. Residential Real Estate
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Most real estate firms and professionals operate and make decisions by integrating hunches or intuition with conventional, retrospective data. While such an approach works, recent studies on modern real estate strategies show that combining non-traditional data with advanced analytics can generate more value and fetch more revenue.
The real estate market is constantly changing and highly volatile. Moreover, investments in this industry don’t come cheap. Hence, investors are looking at real estate data analytics to ensure making intelligent investment decisions. So how is data analytics useful for real estate?
- Optimizing prices for selling and buying, services, and taxes, among others.
- Expediting appraisals with accurate property information.
- Analyzing the profitability of locations with variables such as traffic congestion and distance from facilities such as schools and hospitals.
- Targeting tenants, not just currently, but in the coming years as well.
- Understanding commercial trends and industry competitors.
For investors in the real estate sector who are still wondering how to make money renting houses, well, data analytics is the answer.
2. Healthcare and Medicine
Hospitals, healthcare facilities, and other medical institutions are constantly looking for ways to bring down their operational costs, improve current practices, and deliver the best patient outcomes at the same time. Data gathered from various systems and databases such as ERPs, CRMs, accounting, and patient records among others are thoroughly analyzed and dissected to give them a clear picture of their expenses, purchases, and other factors that impact their operations.
Physicians also use predictive analytics to make accurate diagnoses and create more effective treatment plans for their patients. Special medical devices such as wearable trackers provide physicians up-to-date patient information such as vital signs, making it easy for doctors and medical staff to monitor their patients and respond quickly and decisively in case of emergency.
In the United States, around 2,000 Americans die every day due to cardiovascular diseases based on American Heart Association’s (AHA) findings. In an effort to reduce the mortality rate, several tech companies developed smartphone-based cardiac monitoring tools to track and record heart activity of patients. In the event of a cardiac emergency, the responding medical personnel can quickly retrieve the information from the app and expedite the delivery of the appropriate response and procedure.
Insights derived from mountains of data enable them to spot areas that require expert attention, eliminate inefficient processes that take up many resources, and recognize whether there is a need to purchase medical equipment and more. Big data is also used in researching and developing new medical treatments, drugs, and devices that are safer and far more effective. Data analytics software helps medical teams and researchers recognize disease patterns and formulate ways to contain the infection and prevent an outbreak.
Retail businesses get their data from customer transactions, online and offline inquiries, social media interactions, responses from advertisements, website visits, and more. Information is also harvested from user log-ins, IP addresses, and credit card transactions. With all the information they collected, retail companies then use data analytics software tools to analyze data to predict demand, identify the stimulus, and discover the triggering mechanisms that encourage customers to spend.
Online retail browsers who are presented with product recommendations they find relevant or useful are 70% likely to convert, according to a recent survey by Monetate. In a separate study, 38% of American online shoppers said they will likely look for another retailer if the shop they go to offer them product recommendations they can’t or hardly relate with. Fortunately, retailers don’t have to guess what their customers find relatable or not.
Data analytics is extremely useful in creating and delivering personalized experiences that are unique per customer. Retailers are able to dig deep into their customers’ thoughts, patterns, and behaviors, helping them craft effective and engaging messages, newsletters, and recommendations. Whenever a certain customer visits a retail website, he or she is automatically greeted with an online catalog or a selection of relevant products based on his or her previous visits, purchases, social media interactions, product searches, and more.
One shining example is the sportswear giant Nike. By incorporating advanced technologies including consumer and data analytics, digital demand sensing, and RFID, its first Nike Live concept store is able to predict local demand in real-time. This allows the store to stock on products and items that are on high demand based on local data analytics and digital sales data. It also prevents the overstocking of slow-moving items, keeping the inventory low and spacious to address consumer demand “on a dime.”
4. Banking and Financial Services
The banking and financial services sector is among the industries plagued with the most challenges. These include credit card fraud, identity theft, fraud detection, credit risk analysis, and reporting, Know Your Customer (KYC), and archival of audit trails, to name some.
Many national Securities and Exchange agencies, if not all, are leveraging data analytics to track all activities in the financial market. Using sophisticated and advanced analytical methods including network analytics and natural language processors, these financial watchdogs are able to recognize potentially illegal trading activities and closely monitor fraudulent transactions in the financial markets.
Corporate banks, investment firms, stock exchange traders, retail traders, hedge funds, and other major players in the financial and banking sector rely on data analytics to assess trades, acquisitions, mergers, prevailing sentiments, and other significant movements. The insights they get from the thorough analysis of data help them make crucial decisions such as trading stocks, buying shares, and withdrawing from major bids among others.
Risky transactions are a staple fixture banking and finance markets. With the best data analytics tools, banks and other financial services providers can perform reliable and meticulous risk analytics to support their processes and efforts such as anti-money laundering, fraud mitigation, demand enterprise risk management, and KYC.
Big Data Analytics = Smarter Decisions
It wasn’t too long ago when businesses and organizations had to employ the best and brightest data scientists and professionals with extensive knowledge in various data science verticals to make sense out of data.
Even with data professionals on board, they have to look, analyze and review countless spreadsheets, charts, reports, and audit trails before they submit their own reports, which were basically guesswork using crude and unrefined data analysis methodologies.
That said, no forecast or prediction is ever 100% accurate. But big data analytics took out much of the guesswork and enabled businesses and organizations to generate forecasts that are based on reliable, verifiable, and up-to-date data.
As information becomes readily available and systems are integrated, data analytics software can collect data from multiple sources and provide companies and organizations with forecasts and predictions that are closer to accurate and with as little variance as possible.
CEOs, business owners, managers, and other decision-makers can now make decisive choices with full confidence, knowing their decisions are based on data and not on hunches and feelings.