Where is big data going in 2021?
Big data keeps growing, including vast data sets. Many are crunchable only by machines since even teams of data scientists would require decades to clean, filter and categorize the constant stream of information coming in from all connected devices.
In 2021, experts predict we'll see big changes in how data is gathered, stored and analyzed, according to ZDNet. Data keeps getting bigger, better and more available, but without tools to harness the technology's power, it can be useless at best and a distraction if you're not careful.
Data lakes, which are repositories for raw data, may decline as data warehouses become more efficient at storing digital assets without duplication or wasted space, according to Forbes. Warehouses are now able to separate data stacks into storage and computing. However, data lakes won't go away completely; there is room for the less structured, more flexible lake outside of the warehouse. Some believe the future will see lakehouses instead, which combine warehouse performance, reliability, quality and scale with the structured transactional layer of a lake for a more robust and flexible all-in-one option.
AI & ML
Artificial intelligence is becoming more accessible to the consumer and being used effectively in situations that affect them directly. As the cost of computing continues to shrink, small businesses can harness AI and its less sophisticated cousin, machine learning. A strong focus can be expected to fall on "ethical" and "responsible" AI, to increase transparency and security and make it easier for the C-Suite to embrace and implement AI solutions.
AI and business intelligence will also become more interconnected as AI infiltrates R&D, sales and marketing verticals within organizations. For starters, adoption of AI may be as simple as a setting up an intuitive chatbot capable of learning and applying that knowledge to reprogram itself, or as complex as deep level analysis that can permit you to anticipate and prevent things like fraud or customer churn. In both cases, the impact on BI is clear.
Business professionals can easily become "data pros" thanks to the ever increasing number of intelligent apps, tools, systems and processes available to them. Data literacy is rising, and with literacy come rudimentary analytics skills, powered by predictive analytics tools, augmented analytics, and explainable AI models. Employees are already being upskilled and reskilled for analytics-related jobs as data becomes more and more important. Data culture is on its way, and everyone needs at least a passing acquaintance with the language.
Across the globe, governments are struggling to define ways to manage and regulate fast moving technology, from AI and ML to IoT connectivity and the increasing cyberthreats that come with sending a workforce home to labor and depending on apps to drive processes and even decision-making. Cybersecurity is the responsibility of owners and board members, who must meticulously train workforces and provide constant oversight.
Data tasks can increasingly be automated, allowing workers to focus on more crucial, customer-facing tasks. This is expected to continue and expand, and robotics provides new ways for businesses to operate swiftly and safely. As workers move away from data entry, new jobs appear in analytics and one-on-one customer care.