As many companies look forward to business recovery and operational improvement in 2021, the use of big data and data analytics is becoming more essential to overall digital transformation and business optimization. As data becomes more pervasive, especially given the shift to remote work environments and an increase of online consumer activity, data utilization for analytics purposes is still underutilized. A future-focused approach for businesses everywhere should include the adoption of data analytics capabilities to improve business intelligence and push for data-driven decision-making to enhance company performance.
The use of data analytics in certain business practices will remain indefinitely, particularly in the following areas.
The role of business executives continues to evolve as being more reliant upon data to make strategic business decisions. Data usage is valuable as it enables them to track and measure data for model outcomes, especially when analyzing corporate performance, annual budgeting and general forecasting for the future of the business. To improve executive decision-making through the use of data analytics, business executives and other senior leaders must be in alignment regarding data sources and architecture, as well as key performance indicators that can be measured and utilized with analytical capabilities for strategic decision-making.
Streaming analytics has high capability for enabling real-time decision-making, and as such has been identified as an effective tool to improve overall business operations. Companies can utilize streaming analytics to produce customized dashboards to monitor business activities, including identifying anomalies or unusual activity. The real-time effectiveness of streaming analytics can help companies migrate from reactive decision-making to a more proactive and strategic management of the business. Additionally, streaming analytics can greatly contribute to overall operating efficiency integrated across multiple business departments (finance, marketing, IT, etc.).
Business operations should also consider leveraging cloud and other methods of data storage, specifically through multi-cloud storage and computing environments. Data lakes and warehouses can leverage diverse artificial intelligence (AI) and machine learning (ML) algorithms to allow them to extract the greatest information value from the data and gain insight from the data assets. Such augmentation of AI and ML also carries over for the future of embedded data dashboards, which could become much more prevalent in business operations to provide dynamic insights for making business decisions.
Marketing and Customer Acquisition
Customer acquisition and retention can be greatly enhanced through the use of data analytics. As online activity and the demand for contactless transactions increase, monitoring customer activity and trends can provide important data on the patterns of customer behavior, as well as document any dynamic changes in transactions. For example, sentiment analysis is a prevalent example of how data analytics can be used to measure trends in positive, negative or neutral customer feedback on products and customer experience. Marketing departments can also use customer data analytics to obtain insight into behavior and preferences in order to better understand the target customer base, discover new audience niches and improve overall messaging.
Talent Acquisition and Retention
Analytics can help companies improve their strategy for sourcing, hiring and retaining top talent, especially as talent acquisition becomes increasingly competitive. Data platforms using AI and ML can help increase time efficiency by sorting through resumes and cover letters, as well as data collected from any relevant performance or personality assessments. Human resources departments can also implement data analytics capabilities to track and document employee coaching and performance feedback, or track trends in sentiments and participation regarding company initiatives. Such efforts can be leveraged to determine the best strategies for long-term talent retention.
Data Privacy and Security
As the use of data analytics becomes increasingly essential in everyday business practices, companies must remember to address data privacy and security, especially when handling the data of customers and employees. The evolution of technology has made it much easier to track and profile activity online and in real life through the use of web tracking, phones, smart home devices and smart wearables (e.g., watches, fitness trackers, body sensors, etc.). Additionally, recent data breaches and violations of consumer privacy have elevated the conversation around the need for more regulations. While there is currently no comprehensive federal regulation in the U.S. regarding data privacy and security, some states like California, Washington and New York have already implemented statewide data privacy regulation, which could continue in 2021. Companies must accept fiduciary responsibility for data privacy and assign responsibility for the maintenance of data privacy policies and procedures, especially for any data analytics implementation that involves the data of customers and employees.
As you look to the future, consider how your business is leveraging data analytics capabilities in these areas. For more information about using data analytics to improve your company's everyday business practices, reach out to us at email@example.com.