This post was most recently updated on November 24th, 2022
The torrent of data flooding our business space is phenomenal and big data analytics becomes a key basis of competition, underpinning new waves of innovation and growth. As such leveraging data-driven strategies to innovate, compete, and create significant value becomes key to success.
The history of IT innovation and its impact on business productivity and competitiveness indicates that big data has the potential to turbocharge the ranks of data aggregators and create substantial value for clients.
While it’s tempting to say that the big data revolution isn’t coming – the truth is it’s already underway. Therefore, it is important to understand which type of analytics is a dire need for what facets of the business and how strategically can you unlock the reams of data.
We will take a closer look at the four significant types of big data analytics prevalent in every industry, giving a clear understanding of business goals.
Transform Business Models with Different Types of Analytics
Descriptive Analytics: Descriptive Analytics is considered a useful technique used by organizations to manipulate massive data volumes and simplify them into more understandable charts and trends. Let’s review the numbers, the big data industry has seen tremendous growth with a 62% increase from $169 billion in 2018 to $274 billion in 2022.
And descriptive analytics plays a great role in empowering big data analytics firms to create visually appealing dashboards based on data, helping them see reports like the company’s revenue, sales, profits, or information about certain ongoing industry trends. A comprehensive report on the company’s performance metrics leads to fuel impactful decisions.
Diagnostic Analytics: Diagnostic Analytics uses advanced techniques like drill-down analysis, data mining & data recovery, customer health score analysis, churn reason analysis, etc. to provide a detailed and in-depth insight into the root cause of a problem.
These techniques help organizations derive a clear picture of why their business is behaving a certain way and what better business decisions can be taken to optimize processes at every level of the organization.
Predictive Analytics: Predictive Analytics, as the name suggests, predicts future incidents like market trends, customer trends, and many market-related events based on historical and present data. This analytics is commonly used among businesses to keep track of their past activities and based on them, predict what can be done in text.
Given that the global Big Data services market is likely to generate $103 billion in revenue by 2027, organizations will look to machine learning, statistical modeling, time series analysis & forecasting, linear regression, and AI techniques to improve prediction accuracy and choose the most optimal ways for their business operations.
Prescriptive Analytics: This is one of the most important analytical models used by organizations to synthesize big data, machine learning, mathematical science, and business rule to make reliable predictions and suggest a decision option.
Prescriptive analytics goes beyond anticipating future outcomes by suggesting decision options on how to mitigate future risk and take advantage of a possible opportunity. For example, Google’s self-driving cars company, Waymo deploys this analytical model in self-driving algorithms to leverage semi-autonomous and on-road autonomous vehicle technology.
Business Possibilities with Big Data Analytics: The Next Frontier for Innovation
Risk Management: Using data science technology that embeds predictive algorithms for big data analysis aligned with risk management, organizations can detect patterns that depict a potential cybersecurity threat.
By combining data analytics with advanced statistical modeling, organizations can obtain real-time insights into their risks and based on it, drive risk management strategy. The ‘What-if’ scenario analysis technique also proves beneficial in deciding the most optimal risk response strategy.
Innovation: With big data analytics, organizations are able to see beyond the obvious and use a more data-powered approach to design and manufacturing. The insights derived from data pave the way to innovation. Businesses today, heavily rely on market insights to tweak marketing strategies, business strategies, and many more.
Prediction & Optimization: Predictive analytics has fueled the process of decision-making by opening doors for big data service providers to use their data and look into the future of their businesses. Data have swept into every business function and big data analytics empower organizations to optimize their operational processes at all levels and generate business outcomes in the long run.
Maximizing ROI: Data monetization has been becoming a key component in ensuring sure-fire long-term success. Using data monetization techniques, organizations can drill down to the molecular level of processes to ensure optimization toward the bottom line.
For example, Netflix maximizes its profits and saves $1 billion per year using recommendation algorithms. By transforming business processes and facilitating innovation based on optimized data, you can maximize ROI and step ahead of your peers.
As long as people keep generating a wealth of data, the Big Data industry is expected to witness remarkable growth over the subsequent years. New innovations and developments in data processing, technology-driven tactics, and end-to-end analytics will enable companies to better target customers based on their preferences and boost revenue.
Also, smart big data applications in everyday business life allow responding promptly to market developments, owing to enhanced profit margins.
Choosing the Right Implementation Partner means Driving Better Business Outcomes
In a nutshell, you know your data best but are you implementing big data analytics in your day-to-day business?
An ideal implementation partner has to know your data better to understand your organizational infrastructure and build a strategy that caters to your business needs. The Big Data industry has many facets, Polestar Solutions will determine what your organization looks like from a raw number perspective. In the rush to implement Big Data technology, organizations often end up spending hefty amounts and getting no tangible results in return. Our experienced Data Consulting partners have a well-structured methodology plotted out to find a way to win back the trust of organizations jaded by data breaches and lead towards a higher growth trajectory.
Have a stack of raw data?
We are ready to optimize it using Big data analytics and contribute to your business growth. Speak to our Data Experts and discover how your big data analytics firm with Polestar can be a game-changer.