Over the past decade or so, analytics has been revolutionized. Data became big and exploded.
Companies got access to the cloud. Insightful data visualizations and interactive business dashboards have taken the front seat while spreadsheets have taken the backseat.
The rise of self-service analytics democratized the data product chain.
Now in 2021, advanced analytics is not just for analysts.
Self-driving cars, autonomous delivery drones and robots are the headline face of the digital transformation that we see around us today.
None of these would be available, though without data – the oil of the fourth industrial revolution – and the analytics technology we’ve built to allow us to interpret and understand this.
You might be surprised to know that currently the rate at which data is being produced is twice as much that was produced over decades’ years ago.
That’s true, and most of don’t even realize how much data is being produced just by browsing the internet.
If you don’t want the latest trends in big data analytics to catch you off guard in future, pay attention to these current trends in big data analytics.
Data is becoming pervasive in business – it’s easy to assume all top corporations and enterprises have built core competencies around big data analytics.
The harsh matter though is XYZ percentage of IT leaders say that their big data environment is chaotic.
So now that we understand the importance of the big data analytics; let’s delve deeper into the top eight trends shaping the future of analytics.
Data as a Service (DaaS) is a data management strategy that uses the cloud to deliver data storage, processing, integration and analytics services via a network connection.
DaaS is in some ways similar to Software as a Service (SaaS) that it involves delivering applications to end users over the network rather than having them run applications locally on their devices.
Just as SaaS removes the need to manage the software locally, DaaS outsources most of the relevant data storage, processing and integration processes to the cloud.
While the SaaS model has been popular for more than a decade, DaaS is only now beginning to see widespread adoption.
The reason for a delay in this concept catching on was generic cloud computing were not initially designed for handling massive data workloads; instead, they catered to application hosting and basic data storage. Processing large data sets through the network was difficult in the earlier days of cloud computing, when bandwidth was often limited in scope.
In 2021, with the advent of low cost cloud storage and bandwidth, in combination with cloud based platforms designed specifically for fast, large scale data management, has catapulted the status of DaaS and made it as feasible if not more than SaaS.
What is predictive analytics? It’s a branch of advanced analytics that is used to make predictions about unknown events.
Predictive analytics uses a methodology that uses many techniques from statistics, data mining, machine learning, modeling, and AI to analyze current data to make predictions about future.
It uses a number of data mining, analytics techniques and predictive modeling to bring together the management, modeling business process and IT to make predictions about future.
The patterns found in historical and transactional data can be used to identify opportunities for the future.
The major applications of predictive analytics in 2021 & beyond involve Customer Relationship Management (CRM), Health Care, Collective Analytics, Fraud Detection, Risk Management, Direct Marketing etc.
Quantum computers are able to process information millions of times faster than classic computers.
Even more interesting – even our phones are millions of times are more powerful than the computers that landed Apollo 11 on the moon.
Technologists in 2021 and beyond are exploring the power of quantum computers that are technically speaking 100 million times quicker than traditional computers.
The biggest appeal of quantum computers is the promise of assisting to quickly answer questions so difficult that it would take decades for today’s computers to solve.
Companies and technologists are racing to conquer the massive opportunity but the question arises what is quantum computing?
Traditional computers have only two states: zero or one. Quantum computers, however, allow subatomic particles to exist in more than one state simultaneously so that they can either exist as zero, one or both at the same time.
Quantum bits; also known as Qubits can therefore handle a much vaster amount of information much faster than traditional computers.
Companies like Microsoft, Amazon and Google have proven to us that we can trust them with our personal data.
It’s high time we reward that trust by giving them complete control over our cars, computers and electrical appliances.
Allow Al Intisar to introduce to you Edge Computing.
So what is Edge Computing?
Edge computing is computing that’s done at or near the source of the data, instead of relying on the cloud at one of the dozen data centers to do all of the relevant work.
It doesn’t mean the cloud will disappear. It means the cloud is coming to you.
That said, now that we have defined what edge computing is all about – let’s discuss the advantages of Edge Computing.
With clouds at the center of a whole new operating model, organizations will have distributed DevOps structures and teams to roll out new apps in real time.
5G and Artificial Intelligence are intrinsically linked to edge computing and the hybrid clouding.
In essence, 5G fuels edge computing in the cloud, which will result in more opportunities for automation and AI.
But for this to become a reality it must be understood that hybrid clouds need to be understood as a whole new operating model.
A hybrid approach underpins edge computing, but there is not a fixed number of stops along the way from data to the cloud.
There are many destinations depending on the application. Being cloud native allows federal agencies leverage cloud infrastructure for a wide range of goals.
Agencies need to ensure they shift their entire paradigm, integrating developing apps in a distributed manner and leveraging security right in.
Dark data is defined as the information assets which companies collect across regular business activities but don’t actually utilize it to make evidence based decisions or for analytics, direct monetization or business relationships.
In most cases, organizations retain dark data for compliance purposes only.
There are normally two ways of assessing the importance of dark data. One view is that dark data contains important insights which represent an opportunity lost.
The other view is that unanalyzed data, if not handled well, can cause a lot of problems such as security and legal problems.
The common categories of unstructured data are normally considered as dark data in the analytics sector: customer information, previous employee information, account information, email correspondences and presentations.
So, what is data fabric? It’s one of the up and coming concepts in the analytics sector.
Data Fabric is the architecture and set of data services that provide dependable capabilities across a choice of end points spanning multiple cloud environments and on premise solutions.
Data fabric simplifies and integrates data management across cloud and on premise to accelerate digital transformation.
The evolution of data fabric concept is the use of ‘autonomic’ approaches to explain how modern systems employ Artificial Intelligence/Machine Learning driven automation to adapt based on how they interface with consumer behaviors and data platforms.
Machine learning is utilized to deliver autonomous capabilities with the goal to reduce human labor.
The benefits actually deliver governed and agile analytics to the enterprise on a timely manner, shifting resources from managing trillions of rows of data – to analyze it.
Legacy data governance is somewhat broken in the Machine Learning era. We need to rebuild it as an engineering discipline to drive orders of magnitude improvements.
Most companies do know they need data governance, but are not making any progress in achieving it.
The vast majority of data governance initiatives fail to move pas the drawing board, with Gartner categorizing 84% of the companies as having low maturity in data governance.
For the most part a primary reason why governance has failed in the past is due to the fact that the technology wasn’t simply there to begin with.
We all know the demands of anxious stakeholders who want every right there and then! They need data teams to be agile with data just like we are agile in software development.
But honestly speaking – This is not really agile, particularly since data team’s often get hijacked for “other” work.
No one really trusts the data and simply put it just takes too long.
So summing up – DataOps is the application of DevOps principles to data. A clearer and detailed definition is outlined in the DataOps Manifesto, which states:
Where referred to as data science, data engineering, data management, big data, business intelligence or the like, through our work we have to come to value in analytics.
Individuals and interactions over process and tools.
Working analytics over comprehensive documentation.
Customer Collaboration over contract negotiation
Experimentation, Iteration, and feedback over extensive upfront design.