Preparing for the future of data analytics
Data analytics is constantly evolving, it started with descriptive analytics, which merely described data. Now, we are at a stage where analytics can predict future outcomes in the form of predictive analytics. Thanks to new technologies, like cloud computing, AI, IoT and machine learning, analytics is taking on new forms to complete even more complex operations. Since technology is constantly evolving, we need to be on the lookout for new and interesting forms of analytics that can do far more than current iterations. Hence, in this blog post, we take a look at the future of data analytics and see what is in store for us.
Analytics platforms for the future
Automation will play a huge role in the future of data analytics, making them invaluable to organisations who are looking to streamline and simplify the analytics process.
Augmented Analytics
When machine learning and natural language processing is integrated into data analytics and business intelligence, it creates augmented analytics. This form of analytics is going to play a huge role in analysing data in 2020. Augmented analytics is going to be the future of data analytics because it can scrub raw data for valuable parts for analysis, automating certain parts of the process and making the data preparation process easier.
At the moment, data scientists spend around 80% of their time cleaning and preparing data for analysis. Data preparation is a complex process because data can be drawn from a wide variety of sources. Social media engagement, video views and engagement on forums are just some of the sources of data an organisation can have. The number of data sources grows even more when one considers the proliferation of IoT devices. While IoT opens up new opportunities, it only makes data collection and preparation more complicated than before. This is where augmented analytics can prove to be a tremendous asset for data analysts. The NLP and machine learning component of data analytics allows the platform to understand the data more organically without human intervention. The ability to understand human language makes it easier for analytics to parse out valuable data for further analysis, which makes data preparation much easier.
Augmented analytics streamlines the entire data preparation process, so data analysts spend less time on data preparation and more time generating invaluable insights. Augmented analytics is going to play a huge role in the future of analytics because it automates menial work, and allows the data scientist to focus on more strategic tasks and special projects. It also opens up data analytics to advanced techniques like smart data discovery, which will only make the process even more efficient, making it an integral feature for the future of data analytics.
Relationship Analytics
Data analytics allows data analysts to find the relationship between a pair of variables. However, the main problem is that current data analytics solutions analyse data in isolation. For example, social analytics looks at the connection between a group of people and a certain variable. However, this is a huge missed opportunity because organisations are missing out on the big picture because they are assessing data in isolation.
The future of data analytics will see organisations breakdown and connect multiple data sources from different organisations to make data analysis as comprehensive as possible, which can be done with relationship analytics. With relationship analytics, analysts can connect the dots between data sources that seemingly have no connection with each other. Relationship analytics allows organisations to connect different data sources using multiple techniques to get a comprehensive picture of the problem.
The ability to connect different data sources using several analytical techniques can transform data collection and analysis methods because it allows organisations to maximise the value of their data network and infrastructure. For example, relationship analytics allows organisations to optimise several functions at once, like account renewals, account servicing and pipelines. Salespeople will get a 360-degree view of their customers, allowing them to be smarter and targeted in their marketing campaigns. Relationship analytics is the future of data analytics because it gives organisations that extra dimension to their data analytics procedures.
Decision Intelligence
The future of data analytics is not just about new types of analytics platforms, it is about new intelligence platforms that can help with decision-making. In that regard, Decision Intelligence brings together social science, managerial science and data science into a single field to improve business decision-making. It draws from different disciplines like business, economics, sociology, neuropsychology and education, making it an invaluable asset to organisations. Therefore, it’s safe to say that Decision Intelligence is designed to optimise decision-making and to bring an extra level of quantitative analysis to decision making.
Business leaders struggle in the area of decision making for various reasons. One, most decision leaders do not have all the information laid out before them, making it difficult to make the most informed decision possible. But on a deeper level, humans are not built for optimised decision making because of our tendency to take shortcuts. Shortcuts hinder quality. Decision Intelligence brings the level of optimisation that humans struggle to bring to the decision-making process by themselves. Decision Intelligence can be used to find the optimal solution, i.e. the best solution given the scenario.
Decision Intelligence also deviates from AI because the objective of DI is to place more value on human reasoning and to better understand the long-term effects of a decision, while AI is designed for direct single-link systems. DI uses social science to better understand relationships. Decision Intelligence is going to play a huge role in the future of data analytics because it enables business leaders to make the best decision possible equipped with a better understanding of the human side of the business, instead of putting the sole focus on the technical side.
Continuous Analytics
In the past, data analytics platforms could deliver insights in a few days or weeks, and it would be completely acceptable. However, with the proliferation of IoT devices, the future of data analytics will expect platforms to generate even faster insights, to take full advantage of IoT devices. This is where Continuous Analytics comes into play, it allows organisations to continuously analyse streaming data, so analysts can shorten the window for data capture and analysis. The level of analysis may depend on the speed of delivery analytics teams are looking for.
Continuous analytics is exciting because it provides proactive alerts to end-users or continuous real-time updates. Continuous analytics is analogous to a program running in the background where results are on a predetermined basis. Continuous analytics is possible due to a combination of big data development, DevOps and continuous integration. Using data science and engineering, Continuous analytics has transformed workflow. For example, data analysts allow for improvements in efficiency by reducing the amount of time dedicated to projects.
Continuous analytics is often combined with DataOps to transform software engineering because it allows developers to release software in shorter cycles, facilitating agile development. Continuous analytics is a key part of the future of data analytics because it improves productive efficiency and allows for real-time analysis. Real-time findings are a huge part of the future because real-time data is what is generated by IoT devices. With continuous analytics, organisations can make full use of the data from IoT.
Augmented data preparation and discovery
The future of data analytics will see data discovery and preparation change, in a practice known as augmented data preparation and discovery. Machine learning automation augments and streamlines data profiling, modelling, enrichment, data cataloguing and metadata development, making the data preparation process more flexible. Traditional methods often involve rule-based approaches to transform data. However, augmented data preparation makes the process more flexible because it automatically adapts fresh data, especially outlier variables.
Machine learning augments data discovery because the algorithms allow data analysts to visualise and narrate relevant findings easily. Machine learning also paves the way for several functions like clusters, links, exceptions, correlations predictions and data exceptions without having to rely on end-users to generate all these results. Augmented data preparation and discovery will play a huge role in the future of data analytics because it streamlines data preparation and discovery, giving analysts large sets of clean data.
Looking to the future with analytics
Data analytics is constantly evolving because of the proliferation of new technologies that augment technology and expand its functions. The future of data analytics will see AI expand the capabilities of data analytics technology by bringing automation and understanding of natural language into the picture. This will transform how organisations read and assess their data because they can get the latest insights in a matter of seconds, and at a lower cost. As providers of data analytics, it is imperative to keep a finger on the pulse of the industry and be ready to provide them when clients ask for them. Offering new services takes time and analytics providers need to have an eye on the industry if they want to stay one step ahead of the competition.
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