In the digitally connected world today, organizations require the ability to blend information and insights across people, processes, and diverse data sources to influence business outcomes. To derive actionable data insights that aid enterprise decision-making, Aikyne helps organizations combine technology, data sciences, industry-centric business modeling techniques, and unlock enormous business value. Data Analytics services from Aikyne help categorize, contextualize, scope, and derive data insights, and add value with machine learning paradigms. To harness the incredibly powerful business analytics capability, we identify new ways to combine a high-performance blend of insights, forecasting, visualization, advanced analytics, and more. Leveraging the transformative power of analytics, we bring you solutions that address your business challenges and map your data assets to drive value.
Data, Data and more Data ……… it’s all about Data in a world inflicted with Twin-effect of Disruptive Innovation, Speed of Change and Digital Transformation. Over the years and by every fleeting minute and second; Data has been growing by leaps and bounds. What do we make of it? Moving a step forward we are starting to see Data getting commoditized.
Any digital investment needs an initial assessment to understand the depth and magnitude of the change associated with the transformation initiative. Every change needs to account for the empathy of the end-user (employees, stakeholders, management) and the experience they get from your company. And improving the user experience (UX) of your enterprise software goes far beyond appealing interface screens.
Tableau is a powerful collaboration platform. One can easily create, share and distribute the workbooks and dashboards set up in Tableau. Since people across the organization depend on Tableau, it’s essential to keep it performant and scalable. We need to understand one important thing about the performance of Tableau that it is only as fast as your data source. In case if the data source is responding slower to queries, then Tableau, in turn, would have delayed response.
If predictive analytics is placed at the heart of an automated decision-making process, then it must be approached in a controlled and systematic way. Building a decision-making infrastructure based on predictive analytics is just like any other sort of project, like setting up an IT data center, refitting a factory or restructuring an organization.
