This week’s Economist magazine has the cover story about Uber; the world’s most valuable startup that symbolizes disruptive innovation. The race to reinvent transportation service worldwide is so fast that it’ll dramatically change the way we travel, in the next 5-10 years. While studying the success study of Uber, I was more interested in factors that led to the exceptional growth of the company – spreading to 425 global cities in 7 years, with a market cap of $70 billion.

There are surely multiple factors that contributed to its success, but what made me surprised was its capitalization of data analytics. In 2014, Uber launched UberPool, which uses algorithms to match riders based on location and sets the price based on the likelihood of picking up another passenger. It analyzes consumers’ transaction history and spending patterns and provides intelligent recommendations for personalized services.

Uber is just one example; thousands of enterprises have already embraced big data and predictive analytics for HR management, hiring, financial management, and employee relations management. Latest startups are already leveraging analytics to bring data-driven and practical recommendations for the market. However, this does not mean that situation is ideal.

According to MIT Technology Review, roughly 0.5 percent of digital data is analyzed, which means, companies are losing millions of opportunities to make smart decisions, improve efficiency, attract new prospects and achieve business goals. The reason is simple; they are not leveraging the potential offered by data analytics.

Though the percentage of data being analyzed is disappointing, research endorses the growing realization in businesses about the adoption of analytics. By 2020, around 1.7 megabytes of new information will be created every single second, for every human being on the planet.

Another thing that is deeply associated with the growing data asset is a cloud. As the statistics endorse, data creation is on the rise; it’ll lead to storage and security issues for the businesses. Though there are five free cloud services, the adoption rate is still disappointing.

When we explore why big data analysis is lagging behind and how to fix the problem, it’s vital to assess the storage services too. Though there are organizations that have been using cloud storage for years, the adoption of the same is slow. It’s usually a good option to host general data on the cloud while keeping sensitive information on the premise.

Big Data and Cloud for Business:

As we noted in the previous post, private cloud adoption increased from 63% to 77%, which has driven hybrid cloud adoption up from 58% to 71% year-over-year. There are enough reasons and stats to explain the need for cloud storage and big data analytics for small businesses. Here are three fundamental reasons why companies need some reliable cloud technology to carry out big data analytics exercise.

1. Cost:

Looking at the available options at this point, there are two concerns. Some are either too costly and time-consuming or just unreliable and insecure. Without a clear solution, the default has been to do the bare minimum with the available data. If we can successfully integrate data into the cloud, the ultimate cost of both (storage & analytics) services will turn flat and benefit the business.

2. Security:

We have already discussed that companies have a gigantic amount of data, but they have no clue as to what to do with it. The first thing they need is to keep their data in a secure environment where no breach could occur. Look at recent revelations about Dropbox hack, which is now being reported to have happened. It affected over 65 million accounts associated with the service. Since moving significant amounts of data in and out of the cloud comes with security risks, one has to ensure that the cloud service he/she is using is reliable.

See, there are concerns and risks but thanks to big players IBM, Microsoft, and Google; trust in cloud services is increasing day by day and adoption is on the rise.

3. Integration:

If you look at the different sales, marketing, and social media management tools, they all offer integration with other apps. For example, you can integrate Facebook with MailChimp, Salesforce with MailChimp; which means, your (marketing/sales) cloud offers two-in-one service. It not only processes your data and provides analytics but also ensures that findings and data remain in a secure environment.

4. Automation:

Once you can remove uncertainty, and find a reliable but cost-effective solution for the business, the next comes is feature set. There are cloud services that offer wider automation features, enabling users to save their time and use it for some more important stuff. Data management, campaign management, data downloads, real-time analytics, automatic alerts, and drip management are some of the key automation features that any data analytics architect will be looking forward to.

While integrating cloud with data analytics, make sure that it serves your purpose while keeping the cost under control. Otherwise, the entire objective of the exercise will be lost. As big data becomes an integral part of any business, data management applications will turn user-friendlier and equally affordable. It is a challenge, but there are a lot of opportunities for small businesses to take big data into account and achieve significant results.