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 cannot afford to work with insufficient and inefficient data analytics solutions. Implementing the right strategy, as per the requirements, is imperative. Where do you start?
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 to the system. Observing existing strategies and capabilities to ensure proper alignment with business goals and vision is also required.
There is a possibility that the company might have to entirely change its business approach, while others might only require minor tweaks to the existing system. The result would be to have 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 the established business objectives and values.
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a company’s business processes have captured since the beginning, there would be tons of raw data, some important, some no longer valid.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 to use the right set of data points. Companies must focus on maximising the value of their required data to strengthen their new business models and performance indicators. Taking 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, the focus on conversion rates and customer spending data will be vital, and no other data will add value.
Few leaders argue that even invalid data hold some amount of information that could help support business decisions. On some level, experts do 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, are at its disposal. Identifying the right set of data on priority is key, especially since companies are already overwhelmed with the lack of resources.
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Certain tools cannot handle large data sets, and some solely focus on visualisation. The wrong choice might ultimately lead to an extra burden on the IT employees. Experts recommend businesses to opt for solutions that focus on single-stack capabilities, as this approach includes an analytical database, strong analytic power and visualisation along with an in-built ETL.
The selection of analytics tools is similar to that of a life support system. Without the enhanced support, the process might either be ineffective or tediously slow and incapable 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 no two solution providers are the same. Executive leaders will have to carefully assess and choose the right toolkit that fit the company’s requirements. Having a strong business analytics stack for enhanced results can also support financial planning and productivity.
Once the metrics are decided, data modelling 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. If errors are left unattended, the data models can be burdensome and confusing. Consistent calculating fields and correct dimensional hierarchies will help models work with complex data, efficiently. For beneficial, actionable insights, data modelling has to be done properly. Experts suggest businesses set up analytics goals where data is to be merged from different sources.
Dashboard design is important for several reasons. The most notable reason is for the company stakeholders to be able 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 ultimately, the creation of the data model. Visualisation would be the vital element of how one interacts with data.
A coherent display of complex analytics, including a good style guide with the use of colours, widgets and charts, will go a long way in terms of a smooth, running 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. With the right decision of data design, data relevancy, and information hierarchy, analytics dashboards can prove to be a successful breakthrough.
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 organisation is short-staffed, it is essential that the remaining staff be trained. Without human expertise, the data analytics model might ultimately 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 organisational employees. If financial budgets allow it, companies can hire data analysts or invest in internal training processes.
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When enterprises hold massive amounts of data, use of Artificial Intelligence (AI) solutions can play a critical role. AI-analytics comprises automated anomaly detection that constantly analyses data, identifies errors, and reveals patterns from “noisy” data. Correlating multiple anomalies, AI analytics filter the most crucial insights. AI-driven analytics tools can also depict the cause and effect relationship between different data factors. Additionally, they can monitor a massive amount of metrics intricately, and recommend insights that can help with decisions and business strategies. If enterprises solely depended on big analytics, the subtlest details could be missed.
Decision Intelligence (DI) utilises the insights and patterns from AI analytics and the big data dashboard to help business executives come up with intelligent, creative, and experience-driven decisions. Experts believe that AI analytics fuel best decisions at a large scale and have the potential to boost revenue and streamline business decision processes.
Taking a business to the cloud is a norm and transferring data analytics to the cloud can prove to be cost-efficient and an efficient strategy for any company. Experts warn companies to not dive headfirst into cloud migration, but focus on a well-researched strategy with clear governance framework and security policies. Start small, observe, and proceed accordingly.