The Explosion of Data and Necessity for Predictive Analytics – Businesses around the world today have an aspect of their operations automated. The data generated by companies and organisations on these systems is a gold mine of information that could potentially entitle them with a competitive edge over their competitors. Much like gold ore, this raw extracted data has to be processed first—the step of recovering gold from the raw ore to gain valuable insights is known as Data analytics. However, just like there are many different types of extraction methods for each of the various gold uses, there are also many forms of data analytics to generate the various purposes of this valuable data.
“There are multitudes of data generated by all industries every day and the expected amount of data generated globally by 2025, will reach 175 zettabytes. It’s just the start of the Big Data Revolution.”
The most common data analytics that businesses use since time immemorial is descriptive analytics. It fundamentally explains ‘what’ has occurred in the past by condensing the raw data into understandable segments or reports. Although this may be useful to track performance, it does not help companies understand the ‘why’, ‘what if’ or forecast chances of ‘what might happen’ of their outcomes and decisions. This was partly due to the lack of technological progress in the past to support these types of analytics.
With contemporary technological advancements, predictive analytics is a data analytics strategy that has become a reality. It provides predictions and forecasts from current data amalgamated with historical data databases based on Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This allows businesses to plan their strategies by deriving opportunities and risks with predictive insights based on the identified trends and patterns in data. This data analytics strategy is more in reach than ever for companies to leverage their data and utilise these insights to make better decisions that drive performance and effortlessly reap benefits of the progressions in technology.
This data-driven strategy has already proven to create positive impacts in various industries. In this article, we describe and delve into the endless possibilities in applications of predictive analytics among the different industries (Healthcare, Automation & Manufacturing, HR and Retail).
The shift from paper to electronic-based health records and clinical information in the past decade has made the healthcare industry incredibly rich in data. This digitalised clinical data can be effortlessly compiled into datasets and databases to form the basis for a transformative predictive analytics tool. This tool undoubtedly is capable of outshining the basic descriptive analytics that merely provides information on the previous events of the particular patient in many ways.
Predictive analytics solution is capable of not only generating ultra-precise medical diagnoses for patients but also estimating the precise likelihood of potential outcomes. Likewise, these analytics also equip healthcare professionals with all the necessary information to make better-informed decisions about each patient’s condition based on these insights.
Prediction and prevention go hand in hand, especially in healthcare. Receiving actionable alerts about possible emergencies based on the patients’ vitals and condition deterioration status before they manifest themselves in full-potential is very crucial. These alerts ahead of emergencies allow healthcare technicians to allocate resources efficiently and be well-prepared in rapid response time during critical life or death complications. Thus, this advanced analytics solution allows the healthcare ecosystem, clinicians, administrative staff, patients families and payer/ payee organizations to be one step ahead at all times. Likewise, it could potentially be centralised based upon a myriad of administrative healthcare regulations and compliances for a controlled healthcare standard worldwide.
This predictive analytics strategy also facilitates budget savings to the healthcare industry organisations. A 2017 research study by the Society of Actuaries on the healthcare industry trends reported, more than half (57%) of the healthcare executives at organisations have already employed predictive analytics for notable budget savings. Among these organisations, 26% forecast over 25% or more savings in the span of the next 5 years. Likewise, around 93% of executives who participated in the research acknowledged that this technology is crucial to the future of the healthcare industry.
2. Automation & Manufacturing Industry
Unplanned downtimes are the greatest bane in the manufacturing and heavy machinery industries. Gartner’s study in 2014 suggested that a single downtime cycle normally costs $540,000 per hour lost in workers and assets, which correlates to an astounding $5,600 per minute. Although most business owners attempt to prevent failures or breakdowns through scheduled maintenance tasks routined based on calendar intervals or conditions of the asset, these efforts are not even close to downplay the severity of downtimes. These preventive maintenance strategies do not reduce the frequency or duration of the downtimes any significantly.
In the context of the automation and manufacturing industry, predictive data analytics enables predictive maintenance. It is an AI-powered maintenance strategy aimed to revolutionise the process of maintenance and tackle the conundrum of unexpected downtime due to breakdowns or failure of heavy machinery. The predictive maintenance strategy allows business owners to anticipate impending breakdowns in advance rather than receiving alerts in the event of malfunctions. This enables scheduled maintenance based on prognostics of real-time data combined with an extensive database. Therefore, increasing runtime/ productivity and cost savings, by necessitating maintenance activities to take place only as needed before the predicted breakdown.
Predictive analytics strategy not only minimises the enormous unnecessary cost and time spent on maintenance successfully, but it also guarantees the extended service life of the equipment. Overall, increasing efficiency and making the ideal near-zero downtime in many major industries achievable. Likewise, the data-driven strategy also enables better decision-making and reduces operational stress in businesses that are often influenced by human errors in knowledge or judgement about equipment capacity/ potential.
3. Human Resource
Human Resource (HR) is a crucial contributor to the company’s financial growth, and data analytics is not exactly a new technique in the HR space. This application has been practised in the HR function by the methods and programs used for recruitment, training and employee performance. However, these analytics were fundamentally just based on talent analytics, developed to maximise and match the strategic goals of organisations. While this approach gives organisations data on churn rates and performance, it does not translate the data into actionable insights. Likewise, the strategy lacks acumen from fixating decision-making and operations based on the organisation’s transpired unfavourable outcomes of lost cost without any insights along the way to avoid such effects.
Predictive analytics strategy is the ‘game-changer’ in the HR space. Considering that HR decisions play a significant role in augmenting the growth of an organisation, data-driven behavioural and performance-based predictions add enormous value to the decision making process. This strategy is capable of providing predictions ranging from potential employees’ ‘cultural fit’ in the organisation to turn-over rates of existing employees and the risk of attrition.
The AI-powered strategy also enables organisations to fine-tune algorithms and streamline the recruitment process based on their preferred ‘fit’ to complete their workforce. Likewise, it is capable of identifying their profiles against the current employees’ behaviours and performance to predict the future potential, engagement, performance and attrition rates in the company. This efficiently steers better performance and outcomes in the organisation.
Forecasts drive the supply chain function thus inaccurate demand forecasts lead to unnecessary costs and challenges with regards to inaccurate inventories, improper supply chain management and other consequences like unfulfilled orders, static inventory costs and unsatisfactory customer service leading to losses in potential business and revenue. Retail and logistics businesses ideally aim to have an accurate and efficient demand forecasting system based on real-customer data, that aims to provide customers with the ‘right thing’ at the ‘right time’. These forecasts have been mainly qualitative in the past, focusing on the general market trends, trends, cycles or variations which were often critically inaccurate. Thus, a demand forecasting strategy based on real customer demand data as supposed to qualitative methods has been long overdue.
The predictive analytics strategy in the retail industry is essentially quantitative demand forecasting with the help of Machine Learning (ML) algorithms. It projects accurate demand information and valuable insights about any particular customer’s demand pattern with the help of a historical database consisting of data collected about their order history. Hence, this allows businesses to plan adequate stock inventories for the customer’s foreseeable purchase and avoid any out-of-stock situations which increase customer satisfaction and retention rates.
Likewise, e-commerce businesses are also able to utilise AI-based predictive algorithms to recommend goods or deals precisely to each customers’ interest that may help to drive revenue. By means of a recommendation engine business owners are able to target customers recommending them products based on an analysis of their past data such as the browsing history and personal preferences. This is known as Product recommendation. Businesses can also modify these algorithms to track customers’ data by content-based filtering, collaborative filtering or a combination of both to understand their behaviour better. Thus, this hyper-personalised recommendation engine guarantees the best conversions for your business by offering the most relevant recommendations to every specific individual.
These applications of the predictive analytics strategy also promote budgeting of financial resources and eliminate unnecessary ‘holding inventory’ costs in time and money by allowing production to be adequate and relevant. Not only does it efficiently guide the supply chain function, from inventory production to delivery, it also gives businesses the leg up by allowing them to optimise their schedule by precisely synchronising supply and demand to match scheduled targets.
The wonders of Predictive Analytics are endless, reinvent your business too
Forecasts form the basis of almost every significant managerial decision now more so than ever, in any organisation or company regardless of industry. Businesses and organisations now possess a newfound wealth of actionable knowledge that can be leveraged with the help of AI and ML algorithms. Predictive analytics is a powerful strategy that analyses this multitude of data (current and historical) to accurately predict trends and patterns in the business functioning. It is revolutionising all industries with its endless applications that increase efficiency and plays a vital role in guiding businesses function and strategy.
Now is the time for you to invest and exploit the potential of data-driven technological advancements to your advantage.
Request a call from us today! Here at groundup.ai, we will support and guide you through the process of implementing predictive analytics solutions to your business and increase efficiency simply with existing data. Furthermore, we develop customizable AI solutions to fit your strategic needs and wants.