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The Analytical Ladder of Success

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Chandni U
Chandni U
Assistant Editor

Data Analytics is crucial for business success, and experts recommend these vital factors for a well-strategised implementation

What are we trying to achieve?
What are we expecting from data analysis?
Are we mining random data or looking for predictive insights? 

Leveraging data analytics can be daunting, and with the added pressure of getting company finances on track, companies must work with sufficient and efficient data analytics solutions. Implementing the right strategy, as per the requirements, is imperative. Where do you start? 

Mapping goals

Mapping the business goals in a conference room is a good place to start. Experts recommend executive leaders identify the real area of business value and revenue before implementing data analytics in the system. Observing existing strategies and capabilities to ensure proper alignment with business goals and vision is also required.

The company may have to entirely change its business approach, while others might only require minor tweaks to the existing system. The result would be a system with goals, metrics, and benchmarks for measuring success that is best suited for the digital era. 

This initial framework holds the foundation for a successful data analysis strategy. The core principle of data analytics would be to measure progress and help companies make decisions based on established business objectives and values. 

Data filtration

If we combine all the data that a company’s business processes have captured since the beginning, there would be tons of raw data, some important, some no longer valid. The essential method to enhance a data analysis strategy is using the right data points. Companies must focus on maximizing the value of their required data to strengthen their new business models and performance indicators. Making the right decisions based on crucial metrics that can contribute to business goals is also beneficial. 

For instance, if the goal is to empower customer acquisition, focusing on conversion rates and customer spending data will be vital, and no other data will add value. 

Few leaders argue that even invalid data holds some information that could help support business decisions. On some level, experts agree, but the bigger picture is to set up a smoothly functioning data analytics strategy, which would be unreliable if all the data, good and bad, were at its disposal. Identifying the right data set on priority is key, especially since companies are already overwhelmed with the need for more resources. 

Tool selection

Certain tools cannot handle large data sets, and some solely focus on visualization. The wrong choice could lead to an extra burden on the IT employees. Experts recommend businesses opt for solutions that focus on single-stack capabilities, as this approach includes an analytical database, strong analytic power, visualization, and an in-built ETL. 

The selection of analytics tools is similar to that of a life support system. With the enhanced support, the process could be more effective, faster, and more capable of delivering expected results. Maintaining a list of all required analytics tools can be helpful. Experts recommend an inquiry into the available software tools and platforms like SAS and IBM, as all solution providers differ. Executive leaders must carefully assess and choose the right toolkit that fits the company’s requirements. A strong business analytics stack for enhanced results can also support financial planning and productivity. 

Data modeling

Once the metrics are decided, data modeling is a crucial task. If done incorrectly, the analytics can lead to otherwise avoidable mistakes like ignorance of data sources and calculating fields that could hurt the model. The data models can be burdensome and confusing if errors are left unattended. Consistent calculating fields and correct dimensional hierarchies will help models work efficiently with complex data. For beneficial, actionable insights, data modeling has to be done properly. Experts suggest businesses set up analytics goals where data is to be merged from different sources. 

Dashboard designing

Dashboard design is important for several reasons. The most notable reason is for the company stakeholders to understand the technology and what they are investing in.

A data analytics dashboard can help portray a holistic and enhanced data model. The dashboard would include a comprehensive Business Intelligence (BI) process that reflects KPIs, data requirements, and the data model creation. Visualization is a vital element of how one interacts with data. 

A coherent display of complex analytics, including a good style guide using colors, widgets, and charts, will go a long way in a smooth business strategy. It should also be straightforward enough to convey the information to non-technical users. Experts reckon it is an important challenge for businesses. Analytics dashboards can be a successful breakthrough with the right decisions regarding data design, relevancy, and information hierarchy.

Fostering skills

Implementing data analytics does not mean human expertise is not required. During the aftermath of the pandemic, it is no secret that many companies had to lay off employees, and experts believe that even if the organization is short-staffed, it is essential that the remaining staff be trained. Without human expertise, the data analytics model might fail, as with every technological advancement, technological complications are inevitable.

A good strategy to improve the effectiveness of data analytics in any business is the development of key skills among organizational employees. Companies can hire data analysts or invest in internal training processes if financial budgets allow it. 

AI assistance

When enterprises hold massive amounts of data, using artificial intelligence (AI) solutions can play a critical role. AI analytics comprises automated anomaly detection that constantly analyzes data, identifies errors, and reveals patterns from “noisy” data. Correlating multiple anomalies, AI analytics filters the most crucial insights. AI-driven analytics tools can also depict the cause-and-effect relationship between different data factors. Additionally, they can monitor many metrics intricately and recommend insights to help with decisions and business strategies. If enterprises solely depended on big analytics, the subtlest details could be missed. 

Decision Intelligence (DI) utilizes the insights and patterns from AI analytics and the big data dashboard to help business executives develop intelligent, creative, and experience-driven decisions. Experts believe that AI analytics fuel the best decisions at a large scale and can potentially boost revenue and streamline business decision processes. 

Cloud support

Taking a business to the cloud is a norm, and transferring data analytics to the cloud can prove cost-efficient and an efficient strategy for any company. Experts warn companies not to dive headfirst into cloud migration but to focus on a well-researched strategy with a clear governance framework and security policies. Start small, observe, and proceed accordingly.

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