Data technologies can seem scary—for both businesses and their customers. Consumers’ increasing concerns about how organizations use their data are understandable given Facebook’s Cambridge Analytica fiasco and other high-profile data leaks.
Meanwhile, in the business world, there’s a growing gulf between those who have embraced data innovation and others who have been slow or reluctant to invest in data technologies.
A qualitative study by data analytics provider Humanlytics found that those businesses holding back from investing in data technology were doing so for a variety of reasons, including difficulties collecting, integrating and interpreting data, and human factors such as the “inability to see the immediate value of data.”1
However, businesses that continue holding back risk being left behind, because adoption of data technologies is skyrocketing. More than half (53 percent) of all organizations use big data technologies, according to a 2017 study by Dresner Advisory Services. That’s a big jump from just 17 percent in 2015, with further growth expected—40 percent of current non users plan to adopt big data in the next two years.2
The good news is that big data is no longer just for the big end of town. Today’s data technologies can deliver big results for businesses of all sizes and, importantly, their customers.
In this article, we’ll explore how organizations are using data technologies to gain a competitive edge in variety of ways, but first, let’s take a quick look at some of the technologies.
Demystifying data technologies
At its most basic, data analytics refers to software that applies longstanding statistical techniques to identify patterns, relationships, and trends in data—far more quickly than human statisticians and often on a massive scale.
However, the field has evolved quickly over the past decade to include advanced tools—such as predictive analytics, which uses data modelling to identify likely future trends, and machine learning, a type of artificial intelligence (AI) that uses algorithms to ‘learn’ and improve accuracy over time.
Importantly, these tools can analyze unstructured data, such as social media posts, images, and even human voices, as well as traditional structured data. The likes of Google lead the way in AI and other advanced techniques, but analytics tools are becoming more accessible for smaller businesses, too.
Affordable business intelligence (BI) and data visualization applications now typically include analytics capabilities, presenting data in easy-to-understand formats such as charts.
Major cloud providers, including Amazon and Microsoft, offer analytics and machine learning tools that make it easier for developers to create powerful, data-driven applications in a fraction of the time it would have taken a few years ago—and with equivalent cost savings.
Organizations are adopting data technologies for a range of reasons, including better strategic decisions (69 percent), improved control of operational processes (54 percent), a better understanding of customers (52 percent), and cost reductions (47 percent), according to a study by analyst firm BARC.3
Furthermore, businesses using data technologies reported an average 8 percent jump in revenue and a 10 percent reduction in costs, the study found.
Here are just a few ways that organizations are achieving results like this.
- Integrating multiple datasets for actionable insights: Many organizations use a net promoter score (NPS) to rate customer loyalty, but getting actionable insights from that score can take weeks of research. US automotive glass repair company Safelite AutoGlass has crunched that time down to hours by using natural language analytics on comments from surveys and social media, and aligning the results with NPS data.4
- Improving and personalizing customer service: The Royal Agricultural Society of New South Wales uses analytics in a number of ways to better serve customers who attend the Royal Easter Show in Sydney, Australia. These include sending them personalized emails and providing managers with BI dashboards that display real-time data from a range of sources, so they can react quickly to customer needs and feedback.5
- Improving products through understanding customers: UniKey, a US startup that provides access control software for smart locks, has increased its revenue by 500 percent each year since launching in 2013. One key to its success has been using machine learning to track how customers interact with the smart locks and their environment, then applying these insights to improve its products.6
- Understanding the market and economy: Geografia’s Spendmapp is an Australian cloud application that analyzes anonymized bank transaction data across different demographics and locations. It does this by using Data Republic’s data governance platform to manage data sharing, analytics, and privacy. The result is a service that enables businesses and government agencies—including several local councils—to use Spendmapp’s data to guide planning, policy, investment, and economic development decisions.7
- Analyzing multiple datasets to uncover fraud: Los Angeles County’s Department of Public Social Services deployed a data integration and analytics solution to help in its battle against fraud. By combining and analyzing numerous internal and external data sources, including social media, the county has been able to detect likely fraudsters, accelerate investigations, and save millions of dollars in fraudulent payouts.8
- Reducing costs with predictive maintenance: Data61, the data science division of Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO), has developed a failure prediction analytics tool that’s used by water utilities around the world. By predicting the condition of water pipes, the tool allows the utilities to carry out preventative repairs and, as a result, reduce costs and minimize disruption to water supplies.9
- Optimizing operations: In price-sensitive industries such as transport and logistics, analytics is becoming vital to help companies become more efficient and competitive. For example, Australian freight company Followmont Transport’s analytics solution puts up-to-date data from a range of sources in the hands of managers to help them more effectively load trucks and optimize trips and staffing.10
- Using risk-management analysis for growth: Many businesses, particularly those in the finance industry, use analytics to reduce financial risk, such as bad debts. However, it’s not just about identifying and mitigating risks to and from customers. For example, a risk-management analytics solution has enabled Australia’s Bendigo and Adelaide Bank to quickly and confidently launch new products and set competitive rates to attract more customers.11
Bringing it all together—safely
Most of the above examples involve integrating and analyzing multiple datasets to uncover previously hidden insights, which is why data-sharing and governance platforms are becoming popular among leading enterprises.
Data-sharing and governance platforms enable organizations to safely combine datasets across teams, pool data with industry participants, and collaborate with partners on analytics projects. And it can do all this without risking consumer privacy or information security, thanks to data anonymization and data governance rules.
For example, Data Republic’s platform enabled the hundreds of analysts competing in the annual Melbourne Business School Datathon in Australia to safely share data and collaborate on analytics projects using a variety of major datasets. University and corporate teams were given access to datasets from sources ranging from Westpac and Qantas to Victorian Government health records—with all data aggregated and anonymized on the Data Republic Platform.12
Get in touch with our team today to learn more about Data Republic or to launch a proof-of-concept project.