In the ever-transforming world of finance and accounting, having an edge in quality, efficiency, and timeliness in meeting demands will keep an organization two steps ahead of the competition. However, tasks that require repetition can be tedious and even quite monotonous. No matter how carefully executed, these crucial tasks are prone to human errors. So, how can you minimize such errors? The answer lies in embracing RPA.

A data lake is a consolidated and central data repository of all structured, unstructured, and diversely-sourced data relevant to an organization. It harbors, nourishes, processes, and leverages any data that has value from the standpoint of compliance, analytics, CRM, ERP, business intelligence, and innovation.

Consistency, reliability, speed, and accuracy. These are the litmus tests of any enterprise data strategy. If an enterprise data architecture and tool fail to deliver these imperatives, then anything else they give is, eventually, toothless. But these four characteristics are hard to derive from any enterprise data solution. Especially when the explosion of data, the proliferation of Big Data, and rapid digitalization have made data an unwieldy power for many organizations.

A typical business day is loaded with data. And not just data coming into the formal pipelines. There’s data from everywhere. There’s structured data. There’s unstructured data. There’s raw data. There’s too much data. There’s foggy data. There’s warm data. There’s too cold-to-find data.