What Changed and What Remains Unchanged in Data Analytics
Rapid technological advances in digitization and data and analytics have been reshaping the business landscape, supercharging performance, and enabling new business innovations and new forms of competition. We have made phenomenal progress in the field of IT in recent times. Some of the thorough feats achieved in the tech ecosystem are commendable. Data and Analytics have been the most frequently utilized words in the last decade or two. As such, it’s essential to know what roles in the market are currently evolving, why they are interrelated, and how they are reshaping businesses.
This blog attempts to look over these different stages: simplifying the various buzzwords, narrating the scenarios that were never explained, and keeping an eye on the road that lies ahead. So, without wasting time, let’s take a walk down memory lane.
Need for Business Intelligence Services
This was the uprising of the Data warehouse where customer (Business) and production processes (Transactions) were centralized into one huge repository like eCDW (Enterprise Consolidated Data Warehouse). Real progress was accepted in gaining an objective, in-depth understanding of essential business phenomena — therefore giving managers the fact-based understanding to go beyond instinct when making crucial decisions.
The data surrounding eCDW was transformed, captured, and queried utilizing BI & ETL tools. The kind of analytics exploited during this phase was typically classified as Descriptive and Diagnostic. But, the main limitations observed during this era were that the potential capabilities of data were only utilized within organizations, i.e., the business intelligence activities addressed only what had happened in the past and offered no predictions about its trends in the future.
Big Data
The certain issues of the previous era became more noticeable by the day as companies stepped out of their comfort zone and began their pursuit of a more comprehensive (if not better) approach towards taking a polished form of analytics. Consumers surprisingly reacted well to this new strategy and demanded information from external sources (social media, clickstreams, public initiatives, internet, and more.). The requirement for robust tools and the opportunity to profit by offering them — rapidly became apparent. Inevitably, the term ‘Big data’ came into existence to distinguish it from small data, which is generated purely by an organization’s internal transaction systems. Companies expected their employees to help engineer platforms handle large volumes of data with a fast-processing engine. They didn’t expect a massive response from an existing group of people or what is today better known as the “Open Source Community.”
With the unparalleled backing of the community, Roles such as Big Data Engineers & Hadoop Administrators grew in the job sector and were now critical to every IT organization. Tech firms rushed to build new frameworks capable of ingesting, transforming, and processing big data around eCDW/Data Lakes and integrating Predictive analytics above it. This utilizes the findings of diagnostic and descriptive analytics to detect tendencies, clusters, and exceptions and predict future trends, making it a valuable tool for forecasting.
In today’s tech ecosystem, the term big-data has been utilized, misused & abused in many instances. So technically, ‘big data’ generally means ‘all data’ — or just Data.
Data Enriched Offerings
The pioneering big data organizations began pooling their investments in analytics to support consumer-facing products, features, and services. They attracted viewers to their websites via recommendations, better search algorithms, highly targeted ads, and suggestions for products to buy, all driven by analytics rooted in humongous amounts of data. The outbreak of the Big-Data phenomena extended like a virus. So, now it’s not just tech firms and online companies that can create products and services from data analysis; it’s practically every firm in every industry.
On the other hand, the wide acceptance of big-data techs had a mixed impact. While the tech-savvy organizations forged ahead by making more money, most other enterprises & non-tech firms suffered miserably at the expense of not knowing about the data. As a result, a domain of study Data Science came into play, which utilized exploratory processes, scientific methods, systems to extract knowledge, and algorithms and insights from data in various forms. The tech industry exploded with the advantages of applying Data Science techniques and leverage the complete power of prescriptive & predictive analytics, i.e., to eliminate a future problem or take full advantage of a promising trend.
There’s been a tremendous shift in how analytics are used today. Companies are scaling at speed beyond imagination, identifying disruptive services, encouraging more R&D divisions — many of which are strategic. This requires a new organizational structure: priorities, positions, and abilities. A professional team of data-driven roles ( Data Scientists, Solution Architects, Data Engineers, Chief Analysts ) when under the same roof is a guaranteed recipe for achieving success.
Automated Capabilities
There’ve always been 4 types of analytics: descriptive, which talks about the past; diagnostic, which utilizes the data of the past to work on the present; predictive, which utilizes insights based on past data to anticipate the future; and prescriptive, which utilizes models to specify optimal actions and behaviors.
Although data and analytics include all of the above types in a broad sense, it emphasizes the latter. And it introduces — generally on a small scale — the idea of automated analytics. It offers an opportunity to scale decision-making processes to industrial robustness. Curating numerous models through ML can make an organization more granular and precise in its predictions. The cost & time for deploying such customized models was not completely affordable and necessitated a faster or cheaper approach. The requirement for automation through intelligent systems finally arrived in place, and this idea loomed on the horizon is where Analytics 4.0 comes into play.
Without any doubt the use of AI, ML, and deep understanding will profoundly change the knowledge efforts. We have already seen their innovative abilities in Smart Reply, Neural Machine Translation, Meeting Assistants,Chat-bots, etc., which will be exhaustively used for the next couple of years. The data involved here originated from vast heterogeneous sources comprising of indigenous kinds — one that needs complex training methodology — and especially those that can sustain (make recommendations, improve decision-making, take appropriate actions) itself. Employing data-mining techniques and machine learning algorithms and the present descriptive-predictive-prescriptive analytics comes to full fruition in this domain. This can be taken as one of the reasons why Automated Analytics is encapsulated as the next stage in analytic maturity.
Future of Analytics and What’s Next ???
Data and analytics is filled with the promise that it is run by machines and managed by intelligent technologists and managers. We can reframe the menace of automation as a chance for augmentation: combining intelligent humans and intelligent machines to get an overall nourishing result.
Now, instead of pondering, “What tasks presently employed by humans will soon be replaced by machines in the coming times?” It would be optimistically a question, “What new feats can organizations achieve if they have better-thinking machines to help them? or How can we avert death tolls in affected-prone geographies with enhanced evacuation Artificial Intelligence routines ? or Why can’t Artificial Intelligence-driven e-schools be implemented in poverty-ridden zones ?” Most organizations are exploring “cognitive” technologies — intelligent machines that automate aspects of decision-making processes — are putting their toe in the water. Some are doing pilots to discover the technology. In contrast, others are working on the concept of building a Consumer-Artificial Intelligence-Controlled platform. These platforms utilize the idea of Personal AI bots that communicate with other AI services. There will be no more manual interventions mandatory with an AI-powered system to steer your day-to-day activities.
It would not be a revelation to see either of these tech-making giant leaps in the future. Indeed, there’s an element of uncertainty tied to them, but I’m somewhat optimistic about the future, unlike many. There are always things waiting at the end of the bridge; If you don’t want to see what it is, you probably should not be out there in the first place.
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