Two factors that are dictating how enterprises are conducting business today are – the incessant deluge of data and the proliferation of cloud-based services. Companies need insightful information that is actionable and one that leads to better exploiting market opportunities and ensuring customer delight in a time-bound fashion.

Converting the vast amounts of data that is present in the cloud needs brute computing power and the supreme discerning capability to convert it into informational assets. The sheer volume of data available today along with the complexities of dealing with unstructured data that comprises the bulk of all data available is a herculean task. But today’s customers whether they are bracketed under B2B or B2C need instant results. Thus it’s time enterprises revamp their information management and business intelligence approaches in order to streamline their businesses with the exigencies dictated by the new age business drivers – Big Data and Cloud.

Improved cloud data management solutions

Global IT research and advisory firm Gartner, Inc. describes Enterprise Information Management (EIM) as an integrative discipline for structuring, describing and governing information assets across organizational and technological boundaries to improve efficiency, promote transparency and enable business insight.

EIM can get overwhelming at times since it includes a whole host of services to check the authenticity of data, converting unstructured and semi-structured data into fully structured data, right integration of data, managing its quality at all times, reporting and formulating strategies to best utilize it in a corporate landscape, and so on.

Enterprises need solutions that provide end-to-end data management services encompassing the complete EIM life cycle. The different phases involved viz. architecting, designing, and implementing of humungous data pools need to be taken into special consideration. Formulating the right basis for the architecture of the data is half the battle won. You need the right information architecture to come up with a game plan for utilizing the data and exploiting it for competitive advantage.

Base your information architecture on the following:

• Designing a dynamic data model
• Focusing on data that is reusable
• Effortlessly deploying the Metadata

Extract, Transform, Load

Your enterprise data could be residing in multiple locations in various public and private cloud configurations. Successfully mining all the data itself is hard work. Finding the relationship between the various types of data mined can be arduous. Nonetheless integrating all information is the basis for all EIM strategies.

Your information database needs a scientific approach to handling it. First zero in on a particular data set and then extract the right information from that database to get what you require. The second step involves transforming that data using a set of predefined rules to cleanse the data and convert it into the much-needed form and functionality. The final step involves loading that transformed data into an integrated database wherein it will be referred to every time some action needs to be taken or insights need to be derived.

Analytics that propel businesses forward

Business analytics is a hot domain and corporate head honchos are investing huge sums of money in this direction. Analytics fundamentally involves the use of statistical methodologies to explore vast swathes of data for business gains. Data-driven organizations around the world are benefitting by deploying advanced analytical tools onto data residing in the cloud.

An enterprise’s gain from business analytics depends on the following three factors:

• The quality of data accessed
• Availability of skilled data analysts
• The existence of a data-driven culture

It is the job of the enterprise data analyst to comb through cloud data and come up with a reliable knowledge base to exploit future business opportunities. The statistical methodologies and technologies used are as important as the domain proficiency of the personnel handling the data, to say the least.