Due to the pandemic, public and private organisations are relying on data analytics software tools to complete objectives. Several organisations are now turning to analytics tools to act as a lamp post during a very uncertain time. Given its importance, organisations need to ensure they are making the most of their analytics investment.
Here are some tips to help organisations optimise the rate of findings.
Organisations should align analytics with business priorities
While organisations have invested in data analytics software tools, they have not aligned it with business objectives. This is often the case with thirty per cent of organisations, according to a survey by McKinsey. Taking the time to effectively align data analytics with a wider corporate strategy generates a significant improvement in findings. For example, organisations that scaled AI to match corporate needs were nearly four times more likely to accomplish their objectives compared to those who didn’t.
Improving the rate of delivery with data analytics software tools requires you to break down silos within your organisation. While analytics and AI can do incredible things for an organisation, they will not be used to their full potential if the tools are not utilised properly. This is where the culture of the organisation comes into play. Breaking down the silos between different departments creates a more flexible, dynamic organisation that allows them to make full use of data analytics.
One way to accelerate the rate of data findings is to change how decisions are made in your organisation. Traditionally, frontline employees sent reports to managers and executives who were responsible for making decisions. However, to make the most out of data analytics, we need to reconsider the decision-making process. Teams in the frontline should have the freedom to take decisions when needed.
A shift from top-down decision making will allow you to make better use of data analytics software tools because frontline teams can take advantage of the findings. It is a far more efficient process than compiling reports, then waiting for executives and managers to give their feedback.
To accelerate the rate of findings from data analytics software, you need to start analysing data from its origin point. Data sources are wide and varied. For example, IoT and BI systems all contribute to data collection. Analysing data from its source can help accelerate findings, as opposed to pouring all the data into a data lake. Real-time analytics platforms are particularly useful when analysing data in real-time.
One of the better methods for speeding up the process of data analysis is creating a more flexible infrastructure. When not welded to a framework, organisations have a much easier time taking advantage of the different data sources, improving the rate of real-time data analysis. AI and cloud-based software are two of the key instruments for creating a more flexible infrastructure that will help improve the rate of data collection and analysis.
According to a study, organisations with advanced data analysis capabilities were more likely to use standard tools sets, the survey revealed that over seventy-six per cent of these organisations used standard tooling, whereas eighteen per cent used custom tools.
Standard tools and technologies can help organisations generate findings at a more efficient rate. This is because standard tools offer a greater degree of familiarity, making it easy to hire analytics professionals. Furthermore, the organisation can benefit from better technical support.
Furthermore, standard tools can be optimised to match the requirements of your organisation. For example, one criticism levied at SAS Business Analytics is its extensive technical demands, which can be sidestepped with Selerity BA. Leverage standard tools to improve the rate of findings from data analytics software.
When it comes to data analytics software, the challenge in accelerating data findings lies not in the platform, but factors that support it. Tweaking company culture and data infrastructure can go a long way in accelerating the rate of data findings.
Check out this blog to learn more about data analytics software tools.
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