Today, every business is a digital business.
The evolution of digital businesses demands business leaders to make a leap towards a newer view of data and analytics. As the world moves deeper into the age of technologies like AI, ML, Blockchain, IoT, etc. what data science and analytics trends should businesses be most aware of?
Given that data science and analytics are projected to become critical to any business strategy, what does this mean for businesses looking for growth in 2021?
Here are 7 data science and analytics trends that will be dominant in 2021.
A data mesh is an innovative architectural paradigm. It embraces the ubiquity of data in the organization by utilizing a domain-oriented, self-serve design.
As against conventional monolithic data infrastructures that handle the usage, storage, transformation, and output of data in a single central data lake, a data mesh facilitates distributed domain-specific data consumers and takes ‘data-as-a-product,’ with every domain taking care of their own data pipelines.
Data mesh will take the industry by storm in 2021 because it provides a solution to the shortcomings of data lakes by enabling greater autonomy and flexibility for data owners, allowing greater data experimentation and innovation while reducing the burden on data teams to field the requirements of every data consumer through one pipeline.
According to Accenture, by 2030, over 1 million businesses will monetize their data assets, and over 12 exabytes of data will be transacted each day. Additionally, the data marketplace will unlock more than USD 3.6 Trillion in value. In the coming years, large organizations will either become sellers or buyers of data through formal data marketplaces.
Data marketplaces and exchanges are surfacing as both products and platforms across private and public sectors. They enable the contribution of and access to critical data assets powering a wide array of global data for initiatives such as climate change, wildlife protection, or other public health, social issues. Today, individuals and IoT powered devices generate exponentially more data than ever before. If leveraged appropriately, this will revolutionize the impact of data and analytics and spur completely new data-based innovations. It will also generate new sources of value and revenue via data monetization for businesses that wouldn’t otherwise have a chance to contribute or access unique datasets.
Data democratization means that everyone in the organization has access to data, and there are no gatekeepers that could create a bottleneck at the gateway to the data. The objective is to have everyone utilize data at any time to make insightful decisions with no constraints to access or understanding.
The capability to instantly access and comprehend data translates to quicker decision-making, which further translates into more agile teams and business model innovations. These teams will have a competitive edge over slower data-stingy organizations. When businesses allow data access to everyone across all levels, it empowers individuals with ownership and responsibility to leverage data in their decision-making.
Modern data analytics platforms fail most of the frontline workforce – because insights are not contextualized, easily consumable, or actionable. Businesspeople are still clueless to know which insights to act upon. Businesses expect everyone to be data-driven, not just the analysts or data scientists working in the company. But the tools that work exceptionally well for data analysts and scientists are not extendable. These are too complex for salespeople, customer success people, and almost every other non-technical employee.
Consequently, automated data stories with additional consumerized experiences are foreseen to replace visual, point-and-click authoring, and exploration. The transition to in-context data stories will transform how and where users interact with analytic insight, and the most relevant insights will stream to users based on their context, role, or use.
Today, the digital revolution demands speed, regardless of data complexity. Businesses want to see all the data immediately and continuously. They do not want to get trapped with an IT-established dashboard with rigid drill paths that restrict their capability to instantaneously answer critical questions. Businesses that are driven by revenue growth and capitalizing on the digital revolution recognize that today’s analytics cannot tolerate a punctuated analytic pipeline.
There have always been multiple ways to carry out analytics fast by employing various tools and tricks. However, analytics was always disconnected by separate modules, separate tasks, and independent teams with dedicated skills. But this robs time from what matters most today – timely nonstop actionable information from all the data.
Continuous intelligence is about frictionless cycle time to draw constant business value from all data. It’s an innovative machine-driven approach to analytics that enables businesses to access all the data quickly and accelerate the analysis businesses require, regardless of how off the beaten track it is, irrespective of how many data sources there are, or how enormous the volumes are.
DataOps is a new system for data management. It incorporates development, DevOps, and statistical process controls and employs it in Data Analytics. It fosters collaboration, automation, and continuous innovation of data in a data-powered environment.
DataOps has been mainly aimed at advanced data models. It plays an indispensable role in building best practices throughout a function. Leveraging automation and agile approaches, DataOps builds best practices that allow businesses to deliver value to a range of stakeholders via continuous production.
DataOps enables automation and brings speed and agility to the data pipeline process. Before the data is implemented, data scientists must create data pipelines, test them, and change them. By implementing DataOps best practices, businesses can have a continuous stream of data flowing in the pipeline. This unlocks one of the most critical benefits of DataOps, that is, the potential to gain real-time insights. Gaining real-time insights from the data shortens the time it takes to transform raw data into actionable business information.
Additionally, DataOps helps enhance data quality via version control, continuous development, and continuous integration.
The promise of blockchain is substantial in the data and analytics realm. It addresses two critical challenges. First, Blockchain caters to the full lineage of assets and transactions. Second, it offers transparency for complex networks. Due to the complexity of blockchain implementation, ledger DBMS will cater to a more lucrative option for single-enterprise auditing of data sources.
Business leaders have an opportunity to use smart contracts for trusted data sharing and monetization. According to Gartner, organizations using Blockchain Smart Contracts will increase overall data quality by 50%.
In the coming years, we can expect substantial development and changes in data science and data analytics. Democratization and automation are taking ML/AI models to a much broader audience so that data and ML operation techniques become the critical drivers in the data science landscape.
2021 and the next few years will be overly exciting for the businesses adopting data science. To become and remain competitive, businesses must seek to embrace advanced analytics, and modify their business models, and reinvent their overall strategies to keep pace with the competition. Use these trends to help you do just that in 2021.