Nothing stands still for long in the world of data and analytics. The onset of a new decade this year is no different. As technologies become more accessible and more efficient, our use of them becomes improved. Speed will be a big focus for data and analytics in 2020 and beyond.
Speed, both in terms of the uptake and upgrade of new and older technologies and in how quickly organisations can capture, process and analyse data. The data landscape in 2020, just like all preceding years, is more complex than it has ever been. The number of data sources continues to grow exponentially, and the lifespan of data sources continues to shrink.
Speed is Everything
How quickly organisations can collect, process and analyse their data will be the key driver for success in analytics into the future. It is no longer enough to have low latency analytics, organisations are demanding, and vendors are supplying, analytics in real-time.
Most organisations are not set-up for this kind of speed and will require a change in their mindset and processes to fully take advantage of it. Valuable time should not be wasted manually coding in data pipelines, instead, leverage the technology available and automate these processes.
New technologies, like cloud-based data warehouses, enable organisations to do just that. Automate a once manual and laborious task and take advantage of the speed benefits of data warehouses and cloud computing.
Augmented Analytics and Data Management
Augmented analytics, where analytics systems have some level of input from Artificial Intelligence (AI) or Machine Learning (ML), will become one of the main decision points when organisations choose a vendor. This is all part of democratising data and giving any member of the business, not just data scientists, the power to interrogate data and extract insights.
Augmented analytics can already be seen in some solutions, particularly in the guise of Natural Language Processing (NLP), whereby users can ask questions of the data set using natural language. Although this is currently limited to simple questions, expect the capabilities to expand rapidly and complex questions to soon be on the table. Gartner estimate that 50% of analytical queries will be generated by search, NLP or voice in 2020.
Take advantage by choosing a vendor or solution that gives your users the extra capability that augmented analytics and NLP provides.
Key to achieving real-time analytics, especially for those organisations with a significantly larger and more complex data landscape, will be the adoption of data fabric. Data fabric allows unimpeded sharing of data across distributed computing systems. Where data fabric will grant benefits that semantic layers and data warehouses cannot, is in the aggregation of unstructured, semi-structured and structured data into one unified view, without having to replicate the data into another repository.
Data fabric is created for data in silos. Allowing large organisations to achieve agile data analytics at scale. Although you are unlikely to implement data fabric in 2020, it is certainly something to start exploring if you feel it could benefit you. Expect to see and hear more about data fabric and data mesh as we approach 2022.
Think Beyond 2020
Making the most of your data into the future means thinking beyond 2020. Organisations need to recognise that being open to changes in technology and the data landscape is the key to achieving data and analytics success. Organisational processes should be looked at to see whether they aid or inhibit the adoption of new technologies, and how quickly they do so. New platforms and methods can then be adopted as they become available, rather than once competitors have already done it. Speed is the key to harnessing your data in 2020 and, importantly, beyond.