We are living in the age of Big Data, and data has become the heart and the most valuable asset for businesses across industry verticals. In the hyper-competitive market that exists today, data acts as a major contributor to achieving business intelligence and brand equity. Thus, effective data management is the key to accelerating the success of businesses. For effective data management to take place, organizations must ensure that the data that is used is accurate and reliable. With the advent of AI, businesses can now leverage machine learning to predict outcomes using historical data. This is called predictive analytics. With predictive analytics, organizations can predict anything from customer turnover to forecasting equipment maintenance. Moreover, the data that is acquired through predictive analytics is of high quality and very accurate. Let us take a look at how AI enables accurate data prediction and helps businesses to equip themselves for the digital future.

Data Quality and its 5 R’s

Tech giants like Amazon rely heavily on data accuracy too. Let us take an example of shopping online on Amazon and the common occurrence of product recommendations that appear based on the user’s tastes and preferences. This is an example of how data accuracy helps businesses in keeping track of the customer’s needs and also shows how inaccuracy in data could result in a lack of useful business insights. Thus the quality of data that is collected is highly important in predicting accurate data. Data quality is gauged through parameters called the 5 R’s, and they are as follows:

  • Relevance: Continuing from the example of Amazon that we had discussed earlier, it is important for the data that is collected to be relevant to the customers/ audience and also be aligned with the business niche. While business leaders can decide if they want their AI to go ‘deep’ or ‘wide,’ it is often preferred to use data that is relevant to the business and the users.
  • Reliability: Also referred to as data integrity, reliable data is indicated by the three characteristics called accuracy, validity, and consistency.
  • Recency: This is an age where there is a huge influx of data, and hence businesses must focus on updating their AI systems with new and relevant information. For predictive analytics to work, it is highly important to successfully capture the current trends and to predict the future.
  • Range: Businesses must compile data from a wide range of sources and not just depend on a primary source. This helps in ensuring that the data that is predicted is of high quality.
  • Robustness: Possessing high-quality, robust data enables businesses to maintain accuracy while filling in gaps left by missing values in the AI systems.

Predictive Analytics and its Applications

  • HealthCare: The usage of predictive analytics in the healthcare industry helps doctors to identify patients who are susceptible to developing certain conditions even before they start displaying the symptoms. The 2017 Society of Actuaries report states that 57% of healthcare executives at organizations that already leverage predictive analytics imagine that it will save 15% or more of their budget in the next few years.
  • Marketing: Predictive analytics is a very useful tool for marketers across the world. Without having to manually survey their customers, marketers can easily identify the marketing, communication channels, and product versions that are the most effective.
  • Customer Relationship Management (CRM): Continuing from the previous point, predictive analytics could help with other CRM activities apart from marketing, such as sales forecasting and customer services. Analytical CRM could be applied throughout the customer lifecycle, starting from the acquisition to retention and win back.
  • Manufacturing: The manufacturing industry employs AI for the upkeep of the expensive equipments in use. Using historical data, predictive analytics helps predict future failures and can also detect current risky activities.

The above-mentioned applications of predictive analytics have just scratched the surface. Some of its other applications include sports, collection analytics, risk management, fraud detection, underwriting, and the like.

Data Accuracy as a Business Strategy

Summing it up, using AI and machine learning to achieve accurate data prediction can help companies reap several benefits and also gain an edge over their competitors. It helps them to be among the first to identify the changing industry trends, generate leads and gain customer insights. It also helps them attract new customers using carefully curated marketing strategies and satisfy the existing ones by meeting their standards. Also, using predictive analytics and taking a look at their past decisions, businesses can make smarter decisions. Businesses must pay more attention to data accuracy if they have not done so already. By feeding accurate historical data into AI systems, businesses can maintain data accuracy, and in turn, accelerate their growth.