Data and Analytics trends that will transform business landscape in 2020 and beyond

Modern businesses must deal with colossal amount of data which can be overwhelming. On the flip side, being able to obtain insights from the massive pool of data is beneficial since it helps to make well-informed decisions that propel growth. Brand new BI, data and analytics technologies emerge all the time and it’s important to recognize and embrace those that will help your business gain a competitive edge.

But don’t wait until new technologies grow and mature! Don’t be afraid to engage with them and explore their capabilities. Through trial and error, you’ll be able to find a solution that suits the needs of your company best. At the same time, BI and analytics service providers ought to adopt new technologies to provide their clients with competitive advantage.

We present you the list of data and analytics trends that will shape the business landscape in 2020 and beyond.

Augmented Analytics

Coined by Gartner in 2017, the term Augmented analytics refers to the use of AI, machine learning and natural language processing to enhance data preparation, data analytics and business intelligence.

To glean insights from data, one needs to collect and analyze it. These tasks are the responsibility of data scientists who spend approximately 80% of their time only on data preparation. The remaining 20% is spent on putting this data to good use. With augmented analytics, the initial stages of this procedure can be automated. What’s more, the goal is to get rid of data scientist altogether and even entrust search for insights to AI. Although this should speed up the process of making business decisions, it requires adequate data literacy among employees.

According to Gartner report, augmented analytics are expected to influence the increase in purchasing ML, data science and BI solutions.

Augmented Data Management

Data is collected from various resources so It’s not surprising data scientists spend a lot of time refining it. Augmented Data Management (ADM) allows businesses to cleanse data automatically using artificial intelligence and machine learning. Thus, organizations can eliminate unnecessary and tedious work of data scientists, speed up their productivity and ensure the quality of the data. What’s more, ADM can be useful for data engineers. It will notify them about potential errors and data issues and offer alternative interpretations of data.

ADM will likely cause a big splash during the following years. Gartner predicts that by the end of 2022 ADM will reduce manual tasks by 45%. Further reliance on AI and ML will reduce the need for data management specialists by 20% by 2023.

NLP and Conversational Analytics

Natural Language Processing (NLP) is a branch of AI that makes conversation between humans and machines possible. It’s a technology that allows computers understand written and spoken human language. The most prominent examples where NLP is used are Google, Grammarly, Interactive Voice Response, Siri, Cortana, Amazon Alexa, etc.

NLP grants businesses an ability to inquire into data and gain better understanding of generated reports. Conversational analytics is a technology based on NLP that can provide insight into how users interact with your chatbots or other AI-based interfaces in real time.

Data analytics tools can be demanding, but with NLP, even non-specialists will be able to request information from databases and other less structured sources of information with no effort.  According to Gartner, by 2021, companies will adopt BI and analytics tools for more than half of their employees comparing to 35% of employees that use such tools now. Among new types of users there will be a company’s front-office staff.

Graph analytics

An emerging and exciting form of data analysis, graph analytics works exceptionally well with visualizing complex relationship between data. It utilizes graph format to represent data points as nodes and relationship as edges. This format is the most suitable for finding indirect connections between data points or analyzing data based on the quality and strength of the relationship.

Graph analytics prove to be useful in various fields such as logistics, traffic route optimization, social network analysis, fraud detection, and more. As businesses continue to explore capabilities of big data, graph analytics will become a must-have for deriving a more complex and profound insights. Gartner predicts that in the forthcoming years application of graph analytics will grow at a rate of 100% annually.

Commercial Machine Learning and Artificial Intelligence

Nowadays AI and ML market is dominated by open-source platforms like Python, Apache Spark and R, but, according to Gartner, it’s about to change. Open-source platforms were supposed to democratize the market and make advanced technology available to everyone. Sure, most innovations pertaining to algorithms and development environment over the last five years have occurred on open-source platforms. But open source has some serious drawbacks when it comes to scalability of AI and ML.

At Gartner, they estimate that by 2022 75% of new ML and AI solutions will be based on commercial rather than open-source platforms. Commercial vendors, which at first were slow to adapt, are finally catching up by establishing connectors to open-source ecosystem. Furthermore, they’re introducing features necessary for scaling AI and ML on the enterprise level, e.g. project and model management, transparency, data lineage, platform integration etc. Thus, businesses can combine innovations of open-source platforms with enterprise-ready tools offered by commercial vendors and deploy models in production more efficiently.

 

 

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