Data Analytics stands out as a transformational technical approach of our digital era- and its practice application throughout businesses is growing apace. As data analytics and technology advance, so does the definition of which techniques constitute data analytics. The practice of data analysis tools, BI software, analytics programs, or advanced technology is standard practice in the digital workplace. The adoption of these solutions is usually born out of a dire need to replace inefficient spreadsheet-based reports and data analysis, with the objective of gaining exceptional insights for better decision-making. Businesses require a secure on-premise decision-making platform to bridge the gap between analysis and decision by enabling useful insights that may otherwise go unutilized. Ultimately, the value proposition of business intelligence solutions lies in companies’ abilities to harness them.
At present, time is a luxury industry players don’t have. The global humanitarian crisis, COVID-19, has upended data analytics strategies, sidelined predictive analytics, and encouraged leaders to alter their company’s trajectory. Prior to the COVID-19 pandemic, organizations using traditional analytics tools realized the significance of digital data-driven models. Industrial companies have started to embrace AI techniques and leverage big data and machine learning techniques to ensure datasets with longer timescales and higher granularity. Even though leading incumbents are winning the digital revolution battle, there are a lot of setbacks and challenges that companies experience in the integration of operational expertise into the data science process. Data analytics and business intelligence, widely acclaimed for their predictive prowess and problem-solving capabilities, have become critical navigational tools to address failures.
It is imperative that forward-looking data and analytics pivot from traditional AI approaches to analytics that requires less.
Let us uncover a few key actionable insights for Data Analytics leaders wanting to reconsider the decision-making approach:
- Identifying what business decisions to reconsider, and why
- Prioritizing data analytics over traditional approaches
- Reinforcing AI techniques into decision making
- Reengineering your Data analytics architecture
- Building skills and enhancing capabilities to ensure effective decision making
While uncertainty persists in the business landscape, it’s not surprising that business intelligence solutions help identify potential supply chain disruptions, determine the efficacy of crisis intervention strategies, forecast demand, target the right workforce, etc.
Polestar has highlighted a few elements that help businesses transition to better decisions with a data-driven approach:
Identification: The key areas and operational business activities need to be identified to evaluate performance and allocate resources accordingly.
Research: The decision-making platforms must research business intelligence capabilities to analyze data from multiple sources. This can be production capability, sales figures by product, etc. displayed in visuals dashboards. This facilitates background information that affects the recruitment process, however, this information is not adequate. To ensure it is completely useful, it requires deeper insights & further analysis.
Evaluation: By conducting business intelligence analysis, it becomes feasible to allow users to quickly evaluate various options and check their potential effect on work performance. Also, there is an option of applying different business scenarios before use – that can be further analyzed to see the difference they create. For instance, if a company recruits more salespeople, will sales increase? Will the company require more operatives to meet increased demand for business offerings? Thus, by evaluating the needs & requirements of a business driven by customer demand, it is significant to upgrade business by harnessing technology edged with business intelligence solutions and work on the core areas of business. The road to upgrading business is paved with data and it’s fair to say data-driven initiatives provide fuel to power better decision making.
The choice: Different scenarios can be used to provide a logical decision on how best to proceed. Companies that lay a comprehensive focus on customer-centric choices are likely to generate revenue. The digital-first philosophy can help companies in creating a personalized experience for customers similar to what they are used to in other life aspects. The preferred choice of digital agile business models over traditional ways will help companies remain competitive and relevant.
Review: The outcome of the informed decisions can be monitored through real-time data analysis to achieve mission-critical priorities based on a truly integrated approach.
The Bottom Line
The call to establish a data-driven organization and deploy business intelligence solutions is as loud as ever. Strategic data-driven decisions have a profound impact on business performance and thus, organizations should have a clear view of crucial missions to prepare more effectively for the future. Given that forecasting customer demand is difficult in such challenging times, organizations gravitating towards predictive analytics is extremely helpful. With a fully conducted data-and-model audit, organizations can identify the risk of errors in existing models. This audit can include applying advanced modeling techniques, debugging models, incorporating new data sources, etc. In the long run, organizations should focus on creating high-quality datasets with automated-model surveillance to address challenges amid the whiplash of unforeseen events and reap maximum benefits.
Are you looking to retrieve actionable insights out of your raw data to accelerate decision-making and ensure enhanced business performance?
Let Polestar help you!
