Even the finest fortune teller with the clearest crystal ball couldn’t tell you what the future holds. So, we have gathered up the next best thing, for the purposes of detailing the future of the data analytics field: experts.
That’s right, equipped with an arsenal of knowledge to rival that of the Encyclopedia Britannica, you are on the precipice of learning all there is to know about where the field of data analytics is headed, and what you might be able to do about it.
Read on at your peril if you believe in the adage ‘ignorance is bliss.’ Read on with anticipation if you subscribe to the view that more knowledge is more power.
Trends – Where is Data Analytics Headed?
Artificial Intelligence
Maurice op het Veld, a partner at KPMG Advisory, had the following to share in an interview by Compact:
“New jobs will arise or jobs will change as we need resources to unlock the potential of data analytics. New capabilities required to handle the availability and storage of data will emerge with cloud providers like Amazon Web Services, Google, and Microsoft Azure. Furthermore, new ways to analyze data, e.g., with machine learning and cognitive analytics, will give rise to new professions. Microsoft Cortana and IBM Watson are the frontrunners in this area at the moment.”
He highlights the shifting workplace dynamics brought about as a result of the widespread focus on, and uptake of, data analytics practices and associated technology in the form of AI. While he notes that certain jobs may become altogether redundant, he also points out there is no need to get disheartened – new jobs and functions are also just as likely to arrive with the advent of new requirements for the strategy, implementation, and maintenance of these new technologies.
Within the context of data analytics, AI delivers value through boosting sales via demand forecasting, increasing customer satisfaction through optimizing warehouse stocking, and generating enhanced efficiencies in business operations through process automation – this is all according to Rohit Amarnath, CTO of Vertica, writing for Forbes.
A veritable gold standard of experts, Google CEO Sundar Pichai, predicts that AI will be “more important than fire or electricity” in an article by The Times.
Data Fabric
Most experts agree that the data fabric will be an absolutely essential aspect of data analytics in the present and future tense, according to Gartner. Data fabric connotes an integrated web of data, that responds to changes and flags up actions to different stakeholders at various interfaces of the fabric.
IBM describes data fabric as follows:
“A data fabric is an architectural approach to simplify data access in an organization to facilitate self-service data consumption. This architecture is agnostic to data environments, processes, utility, and geography, all while integrating end-to-end data-management capabilities.”
Why would this be important? The key theme emerging across all of these trends is the value of integration across people, processes, and technology. There is real value to derive from having data accessible in a connected network, rather than as part of individuated silos that do not speak to one another. IBM summarises the key benefits as 1) users themselves being able to access and collaborate on data, 2) key processes of governance and security are automated, and 3) data engineering tasks being automated, with data integration being enhanced across the cloud.
For the first benefit mentioned alone, an IBM report estimates financial benefits to the tune of 5.8 M, and an ROI of 459%. There is not only utilitarian benefit to be gleaned from data fabric, but also financial.
In fact, according to Gartner:
“The data fabric listens, learns, and acts on the metadata. It flags and recommends actions for people and systems. Ultimately, it improves trust in, and use of, data in the organization and can reduce by 70% various data management tasks, including design, deployment, and operations.”
Thus, working with data fabric can also lead to enhanced trust and increased efficiency.
Contextual Data Points
There is a move towards including contextual data in existing analyses of Big Data. This means drawing conclusions based on connections between data points, and not just based on the data points themselves. As such, given the complexity of human behavior, it might be wise to do so, to gain more accurate insights into it. Gartner predicts that:
“By 2025, context-driven analytics and AI models will replace 60% of existing models built on traditional data.”
Integrated Organizational Governance
Continuing the theme of the benefits of connectedness and integration across silos sits the issue of company governance. With an interconnected web of data across the organization, it becomes much easier to make organizational and structural decisions at scale and in an agile manner. Given the current tumult posed to ‘business as usual,’ companies stand to benefit from being able to quickly adapt and re-strategize, as and when needed.
Security & Compliance
We caught up with Benjamin Kaleja, Co-Founder of ZenAdmin. He talked about the importance of integrating across different systems as well. In his own words:
“For us at ZenAdmin, data is the biggest value lever we harvest for our clients. With our platform, we integrate with formerly disconnected systems such as the HRIS, the identity provider, or the Mobile Device Management system and correlate these data points to identify vulnerabilities and increase the security posture of our clients. We can, therefore, for instance, identify whether an employee has been assigned the right security groups, with which device they have logged in and whether the device is patched, and which apps are used by them. These security controls help implement measures to comply with ISO27001, TISAX, or SOC 2 requirements."
Indeed, regulatory compliance is a key aspect of the field. With swift developments in the data analytics field, there is a need to ensure that innovation goes toe-to-toe with compliance measures.
In the high-profile premises of Facebook, too, these security challenges become evident. Mark Zuckerberg released a statement back in 2018, stating:
“We have a responsibility to protect your data, and if we can’t then we don’t deserve to serve you.”
This apology, given in response to Cambridge Analytica misusing users’ personal data, is just one of a series of apologies the Facebook founder and CEO has issued over the years. The full list can be seen on CNBC. The sincerity of such apologies is another question since records have also leaked of his disdain for users trusting him with their data. The occurrence is somewhere in the period 2003-2005 but was first reported on by Business Insider in 2010.
Decisions, Decisions
Data can help with a plethora of business decisions. It may not be so evident for those in positions to leverage these capabilities. In fact, according to Alan D. Duncan, Distinguished VP Analyst, Gartner:
“While data and analytics leaders, such as chief data officers, recognize that there is an inherent need for data-driven decision making, linking this demand to measurable business objectives and outcomes is an existing challenge.”
Often the challenge is in operationalizing the move to data-driven decision-making. The phrase, “If it ain’t broke, don’t fix it” comes to mind. With organizations running smoothly for so many years, where is the incentive for changing to data-driven decision-making?
As we have seen in the above statistics, there are both financial and efficiency gains to be achieved. Organizational efficiency may not make for the most grabbing headline, but it can make or break the execution of your company’s strategy.
Recommendations
“Now is the time to anticipate, adapt, and scale the value of your D&A strategy by monitoring, experimenting with or aggressively investing in key D&A technology trends based on their urgency and alignment to business priorities.”
So says Rita Sallam, Distinguished VP Analyst at Gartner.
While D&A may be recognized as key by leading business publications, there is concern that perhaps not all organizations are making the best use of it. It is down to the leaders of organizations and leaders of the information technology divisions also, to ensure that data practices are embedded throughout the organization. This portends huge benefits when capitalized on correctly. As put forth by Douglas Laney, Vice President and Distinguished Analyst at Gartner in the UK,
“CIOs need to go beyond thinking and talking about information as the new oil. Information has unique economic characteristics that render it potentially much more valuable to their business than any fossil fuel.”
This somewhat echoes Sundar Pichai’s aforementioned commentary on how vital this brave new frontier in the business world means. It isn’t for the fainthearted, as it will require change on an exponential level. The resounding message from all corners of the leading business journal circuit is this: Data is prime. Find a data analytics company to prepare for the future.
FAQs:
Q1. Is data analytics a good career in the future?
Data analytics seems like a fairly future-proof career if Gartner analyses are anything to go by. There are several different types of roles, and these will likely require a data science background. Data analytics roles are considerably well-paying.
Q2. What is the future of big data analytics?
The future of data analytics has a few components. There is a big focus on the following: AI, Security & Compliance, Contextual Data, Decision-Making, and Data Fabric and Governance. A common thread throughout these is, ironically enough, the fact that data is predicted to be increasingly interconnected and less silo’ed.
Q3. Will data analytics be automated?
Yes. AI is likely to come into play in a big way. This means that automation will make some roles redundant while creating efficiencies and potentially opening the door for new positions to emerge.