How Augmented Analytics is Preventing Misleading Interpretations?

Augmented analytics is a blend of ML (machine learning) and AI (artificial intelligence) algorithms that automates data preparation and management. It provides insights across an organization. Augmented analytics focuses on minimizing the human element and empowering ML. This helps businesses to process data rapidly on a large scale and reduce the time taken in searching trends and patterns in the data.

Why traditional business intelligence is not efficient enough?

If you use a traditional BI, you can face a lot of shortcomings. One of its major disadvantages is that there is a lot of manual work that is needed to be done before even starting the analysis. A company can hire an expensive data scientist and assume that it will save them a lot of time. But data scientists spend most of their valuable time cleansing the existing data manually before working on it.

Traditional BI platforms rely completely on the data scientists to cleanse and search for the exact information and the right answers within the data. The problem with this type of approach is that if you don’t know exactly what you’re looking for, the probability of finding it becomes even less. And with so much human involvement and overreliance on it, biased opinions are bound to creep into the data. Also, the data scientist might make assumptions based on different factors unknowingly and use the data to support the same.

Contrary to the above, augmented analytics enhances data sharing, data analytics and business intelligence with the help of machine learning. Also, while using a platform like augmented analytics, you can convert these insights into measurable and actionable steps. Augmented analytics makes these crucial data insights accessible to all small and medium businesses. In fact, even if these companies do not have any dedicated data scientists to work with.

IBM Cognos Analytics Features

With IBM Cognos analytics, you will get all the features of augmented analytics used to prevent misleading interpretations.

• Cognos Analytics can help users to clean and shape their data quickly and easily. It can re-use existing assets like FM models and OLAP sources etc. and help them get their data on point.
• It harnesses augmented analytics which uses machine learning to do all sort of cleansing and sorting of data points.
• With Cognos Analytics, you can dodge accepting incomplete or misleading interpretations by using analytics tools.
• Augmented analytics in Cognos Analytics can spot trends and patterns even before they become an issue. This toolset can also identify wrong and irrelevant insights easily in comparison with manual analysis.
• It can provide quick and easy access to data sources based on access privileges of the users. Cognos analytics come with a feature of pervasive search which makes it easier to search relevant data from the data sources.
• Business users can combine external data with IT-curated secured data with the help of data modules and security filters only with IBM Cognos analytics.
• Cognos analytics can bring higher strategic value to your reports and forecasts, and can share insights quickly and easily with various stakeholders.
• It simplifies data preparation with built-in intelligence that recommends different types of data joins. IBM Cognos can find different ways to blend data from multiple sources
• IBM Cognos analytics can control who views and updates the data modules in the system with various security filters. This reduces the risk of data modelling errors.
• Using Cognos Analytics, users can receive short cuts that use augmented intelligence to reveal better, more actionable business intelligence insights. These can be beautifully visualized as well.

Analytics is the core of any digital business. Data complexity is increasing by the day which makes it difficult to identify what is most important. For this, and many more reasons, IBM Cognos analytics is the way to go, for taking the best actions possible.