3 Key Technologies for Data Analytics

3 Key Technologies for Data Analytics

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Companies have been collecting and analyzing data for decades to tighten their operations, better understand their customers and increase revenue. But the modern-day tech landscape is a whole other animal, generating around 2.5 quintillion bytes of data per day.

Data presents companies with opportunities to glean sharp insights, but a lot is involved in getting to the insight stage. More tools and workflows exist than ever before to help companies get there, but many organizations still fall flat.

If your company is struggling to get value from its data, turn the tide by implementing these three key data analytic technologies.

Data Integration

According to a 2017 SAP study on the challenges of big data, 85 percent of organizations struggle with data from a variety of locations while 72 percent said their data was so complex that it limited agility. Yet, 96 of companies agreed that data analytics was among their most important technologies.

Without a way to connect widespread data into a central analytics platform that an entire organization can access, achieving proper data hygiene (i.e. avoiding duplicate data, replacing, modifying, or deleting messy data, removing inconsistencies, etc.) is extremely difficult. When data isn’t unified and accessible, organizations can’t move fluidly because they’re not working off the same information.

Having a data team can certainly remedy some of these pain points, but do you really want all those expensive salaries stuck in the weeds doing tedious work all day? You’d probably want them focusing on higher-level data tasks that drive business goals.  

AI-Powered Search-Based Knowledge Discovery

Regardless your company size or industry, you likely collect a lot of data. So much that it becomes difficult to extract valuable insights. According to 2016 Forrester Research, 74 percent of companies say they want to be data-driven, yet only 29 percent say they’re good at connecting analytics to action. It’s a tough reality to swallow for companies enthusiastic about data analytic initiatives. However, it’s not surprising given how often companies initiate data efforts without doing the pre-work involved in ensuring actionable insights can be mined from the trove of data they’re excited to collect.

Thankfully, the progress of AI and its integration with data analytic tools simplify this process. For example, data analytic tools like ThoughtSpot leverage machine learning to thoroughly understand a company’s data. Users are able to search for answers to their questions in simple terms and are presented with digestible insights through charts and graphs. The more users search over time, the better the tool understands the information that’s most important to the company.

NoSQL Databases

Traditional relational databases using SQL have been the preferred way to manage data for decades. However, NoSQL databases are increasingly being leveraged because they’re faster and allow companies to more efficiently scale data efforts. As MongoDB points out, this is because NoSQL databases can handle varying amounts of structured, unstructured and semi-structured data. NoSQL databases are also open-sourced, meaning it’s easier for engineers to develop, implement and share software quicker and more affordably. With NoSQL databases, businesses are more flexible in responding to both internal and customer needs compared to using traditional, rigid databases.

To compete in the modern data analytic game, it’s important to preface any tools, processes or workflows put in place with a company culture that believes in data and its power to help every role measure goal performance and make proactive decisions. There are undoubtedly more data analytic technologies companies would be well-suited to implement, but these three will undoubtedly change your data game for the better.

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