Today’s business landscape is a tumultuous one, with 29% of UK businesses citing economic uncertainty as a key factor in affecting turnover. Success in this climate means making the right decisions fast and confidently, staying ahead of competition is often down to a series of small, tactical decisions rather than one large pivot.
To achieve accuracy and speed when calculating this invaluable business insight, data plays a key part in demonstrating that recommendations come based on robust business learnings. Data-driven decision making is truly becoming make or break, and both predictive and prescriptive analytics are a part of this.
Senior VP of Global Partnerships at Oxylabs.
The difference between predictive and prescriptive analytics
These two forms of data analytics share many similarities, for example, both methods use Machine Learning (ML) and AI solutions to analyze vast amounts of data so leaders can make informed, data-backed decisions at speed.
Despite these overlaps, some key differences need to be considered. Predictive analytics has been around longer, and is the analysis driven by big data sets to predict what could happen. In this form of analysis, algorithms and AI tools are trained to make predictions based on existing data patterns.
Prescriptive analytics takes this one step further by telling decision-makers not only what might happen but also advising them on next steps. This makes a big difference and provides a new level of value, acting as a partner to the business, beyond just outputting pure data.
It accelerates timely decision-making, which can make all the difference in the fast-paced and dynamic economy businesses face today. With this in mind, it’s no wonder that prescriptive analytics has gained particular traction in recent years and is expected to see even wider adoption going forward.
Advanced data analytics in the everyday life
Advanced analytics has already impacted everyday lives in more ways than people realize. According to Harvard Business Review, the applications vary from detecting malfunctions in manufacturing to behavioral targeting in marketing. Below are use cases that have gained the most traction in the last 12 months.
Predictive analytics has been used for years by healthcare professionals to forecast disease outbreaks, monitoring case numbers and charting patterns globally. For pharmaceutical companies, they can use this tool to analyze historical data in order to predict the outcomes of drug development processes and clinical trials, including helping to improve operational efficiency and bring drugs to market faster.
Outside of medicine, we’re seeing predictive analytics be used to solve the growing issue of cybercrime. Last year a PwC survey found that 75% of surveyed executives said cybersecurity posed a risk to their business. This is in part due to the fact that hackers are continuously developing new techniques; something the retail industry has been plagued with this year.
In response, data scientists are using prescriptive analytics to actively pre-empt attacks. To identify behavior patterns, they leverage security logs, network traffic, threat intelligence feeds, and other data sources. Once this data has been collected, the teams then look out for anomalies that could indicate a threat.
Prescriptive analytics enables data scientists to execute security measures proactively, putting them ahead of the hackers.
Prescriptive analytics also plays a key role in retail and e-commerce marketplaces to personalize US customer experiences. For example, Amazon’s ML-powered recommendation engine predicts what users might buy next and suggests discounts, delivery optimizations, and inventory adjustments to maximize conversions.
This level of analytics has become an essential part of customer retention and revenue growth within the retail industry.
What’s holding data economy growth back?
These examples are by no means an exhaustive list of current data analytics use cases. Predictive and prescriptive analytics are transforming supply chain management, logistics, travel and transport, heavy industry, e-commerce marketing: it would be impossible to include the whole list here. On top of this, the use of prescriptive analytics is expected to increase.
However, there are some factors holding this back from effective implementation in markets, as some of the potential “handbrakes” on the use of data analytics need to be removed. One challenge we’re currently seeing is the human factor – due to data illiteracy, many cross-function teams still lack the knowledge and skills to interpret predictive and prescriptive data correctly and this holds them back from achieving their true potential.
There is another “handbrake” that is potentially even more important – a lack of quality data. According to IBM, it’s estimated that $3.1 trillion of the United States’ GDP might be lost due to bad data annually, with a shocking 1 in 3 business leaders reporting that they don’t trust their own data.
You don’t need expertise to realize that successful data-based decision-making needs the data itself to be reliable, timely, and relevant. On top of these, companies can run into several data quality issues. One common problem is depending exclusively on historical data for predictive and prescriptive analytics, which leaves companies addressing yesterday’s challenges instead of tomorrows.
Another commonplace issue is internal data being siloed. This can result in an incomplete picture that produces skewed results and insights that can’t be relied on. More generally, although internal data can be very valuable for answering specific questions, businesses also need to incorporate external data if they are to truly benefit from advanced data analytics.
The unexpected role of web scraping in data-driven insights
This leads us to the role of web scraping in advanced data analytics – a method of accessing and collecting publicly available data in an automated way. By utilizing APIs or other scraping solutions, businesses can collect unstructured data from a range of sources, such as, e-commerce sites, news media, forums, travel engines, job boards, and parse it into analyzable datasets.
Through web scraping, organizations can collect granular data which is valuable for both predictive and prescriptive analytics. Multifaceted external data can complement internal datasets collected by companies and provide insights into consumer sentiment, market trends, pricing fluctuations, and competitor strategies.
Web intelligence collection has long been an invisible industry that provides a competitive edge for different economic sectors, used by millions daily. For cybersecurity professionals, web intelligence provides vital insights into emerging threats, vulnerabilities, as well as the tactics, techniques, and procedures (TTPs) used by cybercriminals.
Meanwhile, in e-commerce, players large and small use web scraping every day to dynamically adjust pricing and gather insights into customer interest and sentiment. In other words, data scraping is built into the everyday lives of millions of users to get better prices, services and experiences.
By employing the newest AI and ML-powered scraping solutions, it’s now possible to collect data in real-time. Crucially, syncing timely external signals with internal data analytics solutions is essential for effective prescriptive analytics.
As mentioned previously, businesses don’t want to be creating plans of action for yesterday’s data, they need to know that following the advanced analytics recommendations is the right response to conditions right now. Not factoring in the developments in real-time could be fatal in terms of decision-making.
Data-driven decision-making is the new norm
In today’s world, predictive and prescriptive analytics are fast becoming indispensable. A few years ago, they were only considered “nice-to-haves” that gave companies a competitive edge, but today they’ve become essential tools to keep up with rapidly changing market conditions and global competition. If businesses have yet to embrace advanced data analytics fully, it’s becoming increasingly important to do so.
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