As augmented analytics advances to enable better business decisions, it comes with its benefits but also a list of challenges
With the advancements in data processing tools and the adoption of technologies like augmented analytics, Smart Data market is expected to reach $31.5 billion by 2022, says Frost & Sullivan’s recent analysis – Turning Big Data to Smart Data: Emerging Opportunities.
Augmented analytics automates data insights from Big Data and converts it into ‘smart data.’ This technique provides greater data clarity, but the most important advantage in getting Smart Data insights from Big Data, is that data scientists may not always be required for analytical insights- business users will be able to conduct intelligent analysis as well.
Smart Data helps businesses reduce the risk of data loss and improve a range of activities such as operations, product development, and predictive maintenance. “Markets such as the US, the UK, India, and Dubai have rolled out several initiatives to use Artificial Intelligence (AI) and machine learning-powered data analytics tools to generate actionable insights from open data,” said Naga Avinash, Research Analyst, TechVision.
With this smart data evolution in data analytics, companies such as Datameer, Xcalar, Incorta, and Bottlenose are focusing on developing end-to-end smart data analytics solutions. For these companies to grow, leveraging a data monetization approach becomes important as it allows businesses to utilize and add value at every point in the data value chain.
The biggest benefit of using augmented analysis for Big Data is that it reduces the time and complexity of deriving valuable insights from new data sets. With this, the business manager can act on real-time IoT data, geographic data or even a live view of business transactions in just minutes. This makes real-time commerce and customer engagement possible.
Some challenges with adopting augmented analysis include:
Massive costs: For some instances, adopting augmented analysis adds to costs. With pricing based on the amount of data to be analyzed, it’s important to not run the computation repeatedly on every piece of data, but only on the databases of unstructured data to be analyzed.
Building trust: A major challenge that leaders will also face is of trust with employees. Organizations have to address their concerns about the changes their current jobs will undergo once augmented analytics projects are off the ground. It’s important to focus on how these tools provide employees with more time to take action and optimize performance.
Keeping data protected: With augmented analysis, while it becomes easier to draw insights from big data, it also can be more vulnerable to data hackers. The tool can be tampered to support pseudo-insights and harmful recommendations. If not protected properly, hackers can find ways to interact with the systems that derive the crucial ‘business insights.’
Governing raw or big data: While using augmented analysis, it becomes very important for CMOs to have reliable data in place. This is very tricky because manual data preparation can lead to biases and can be a risk to effective decision-making.
If done well, augmented analysis for smart data has the potential to create direct pathways to insights to help make real-time, data-driven decisions that boost productivity.