Data and Predictive Analytics
Data and predictive analytics refers to the process of collecting, processing, and analyzing data to identify patterns and make predictions about future events and trends. This process utilizes machine learning algorithms and statistical models to identify patterns in data and make predictions based on those patterns. Predictive analytics can help businesses in many ways, such as optimizing operations, reducing costs, improving decision-making processes, and identifying new opportunities for growth.
The use of data and predictive analytics has become increasingly popular in recent years due to the rise of big data and cloud computing technologies. According to Forbes, the amount of data created, collected, and consumed increased from 1.2 trillion gigabytes in 2010 to 60 trillion gigabytes in 2020 (roughly a 5000% increase), and with more data available, the statistical analysis performed within data and predictive analytics has become increasingly accurate, unlocking new potentials and insights [2]. Additionally, 94% of enterprise analytics professionals say that data and predictive analytics is critical in an organization’s digital transformation process [1].
The four main components of data and predictive analytics are Data Collection, Data Processing, Machine Learning/ Statistical Modeling, and User Interface. Data Collection involves gathering data from various sources such as sensors, databases, or social media platforms. Data Processing then cleans and transforms the raw data into a format that can be used for analysis. Machine Learning/ Statistical Modeling leverages specialized algorithms to identify patterns and trends in the data to make accurate predictions. Finally, the User Interface allows users to interact with the system by controlling devices or displaying insights from the analysis.
Key Benefits:
Data and predictive analytics yield a variety of different benefits specific to the solution being implemented. Some of the most common benefits of data and predictive analytics include: Enhanced Efficiency, Improved Decision Making, Reduced Operational Costs, Enhanced Customer Experience, and Risk Management.
Enhanced Efficiency: By utilizing data and predictive analytics to identify patterns in data, business processes and workflows can be optimized or automated. Additionally, predictive analytics can improve supply chain management by predicting demand and optimizing inventory.
Improved Decision Making: Data and predictive analytics can help businesses make informed decisions by identifying potential risks and opportunities. By analyzing historical data, predictive analytics can provide insights into future trends and patterns that might not be immediately apparent, allowing businesses to optimize their workflow performance, anticipate requirements, mitigate risks, and forecast the future more efficiently than ever.
Reduced Operational Costs: With the use of data and predictive analytics, organizations can reduce operational costs by identifying inefficiencies and streamlining/ automating processes across the company. By forecasting future trends based on historic data, predictive analytics can help businesses optimize inventory, improve delivery times, increase sales, and reduce the cost of supply chain and inventory management.
Enhanced Customer Experience: By analyzing customer data, predictive analytics can help companies identify which products are most likely to be purchased by which customers, allowing them to tailor their marketing campaigns accordingly and maximize ROI. Also, by identifying trends in customer behavior, companies can create more personalized experiences for their customers, increasing brand reputation and customer loyalty.
Risk Management: By analyzing large amounts of data from various sources, predictive analytics can help organizations identify potential issues before they occur, meaning proactive measures can be taken to mitigate those risks. For instance, the financial industry leverages predictive analytics to identify patterns that might indicate fraudulent activity allowing them to prevent fraud before it occurs, whereas manufacturing and construction companies use predictive analytics to estimate when machines are at risk of failing, allowing for proactive maintenance and reduced downtime.
Market Outlook:
Data and predictive analytics is currently considered one of the fastest-growing markets in the world, with a projected compound annual growth rate (CAGR) of 25% from 2021 to 2030. In 2021, the global data and predictive analytics market was valued at $24.63 billion and is expected to reach $148.45 billion by 2030 [3]. Currently, the leading providers in data and predictive analytics include IBM, SAS Institute, Microsoft, and Accenture.
Predictive analytics is being adopted by various industries to solve business problems and optimize processes. For years, many financial service companies have utilized machine learning and quantitative tools to sift through vast amounts of data and predict credit risk and detect fraud. The medical industry has recently started using big data and analytics to improve health in a variety of ways, such as patient condition prediction, drug discovery, and clinical testing optimization. Furthermore, retailers are leveraging this technology to correctly anticipate their customer’s desires, allowing them to provide products faster, increasing profits and customer satisfaction. Many other industries are using data and predictive analytics to improve project management, reduce costs, and increase efficiency, and as companies collect more data, this analysis becomes increasingly valuable.
BwB Advisors' Methodology for Adopting New Technologies:
The business world and the technological world must evolve parallel to each other. No matter your organization’s industry, implementing new technology in an organization is vital for staying competitive in the dynamic markets of today. Nevertheless organizations should proceed cautiously in deciding which technologies are needed and how to implement them — one size does not fit all.
At the BwB Advisors, our Technology Adoption Methodology (TAM) follows a 6 phase process and provides a complete roadmap from identifying an advantageous technology all the way through its deployment so that your organization can ensure success.
To learn more about us and our Technology Adoption Methodology click here.
June 2023 / Insights / By: Michael T. Casarona
References:
[1] Anthony, J. (2023, February 16). 70 relevant analytics statistics: 2023 Market Share Analysis & Data. Financesonline.com. Retrieved March 26, 2023, from https://financesonline.com/relevant-analytics-statistics/#:~:text=60%25%20of%20companies%20around%20the%20world%20use%20data,using%20and%20data%20and%20analytics%20effectively%20%28MicroStrategy%2C%202020%29.
[2] Luenendonk, M. (2023, January 5). Data Analytics statistics. the whole industry at glance — 2023. FounderJar. Retrieved from https://www.founderjar.com/data-analytics-statistics/#:~:text=Key%20Data%20Analytics%20Statistics%20for%202023%201%20The,has%20increased%20by%2050%25%20since%202016.%20More%20items
[3] Quince Market Insights. “Global Data Analytics Market Size to Grow with a CAGR of 25% from 2021 to 2030.” GlobeNewswire News Room, Quince Market Insights, 18 May 2021, www.globenewswire.com/en/news-release/2021/05/18/2231324/0/en/Global-Data-analytics-Market-Size-to-Grow-with-a-CAGR-of-25-from-2021-to-2030.html
