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How to Embrace Digital Transformation in Data Management

By   /  April 18, 2018  /  No Comments

Click to learn more about author Avneet Narang.

The only way that companies can stay in the race in today’s severely competitive business environment is by implementing advanced tools and technologies in daily operations. Top of the list of priorities should be Data Management. This is primarily because of the deluge of data generated from various sources – mobile, IoT and Cloud. Adding to this scenario is the inclination of companies to create digitally enhanced product lines and services to improve operational efficiencies, further enhancing data generation. In fact, studies have shown that more than 50% of the global 2000 businesses will incorporate modern technologies in their systems by 2020, simply to be on top of Data Management.

However, before making the transition from traditional Data Management techniques to current technologies, an in-depth study should be made of which of the many options available in this field should be implemented. Comprehensive and total changes might be unwarranted. To really optimize operational efficiencies, an analysis should be made of which areas of data processing need to be modernized and which technologies would be most suitable for ringing in the change. Further, IT departments should assess whether the existing Data Management infrastructure is sufficient to meet the requirements of the transition to the new age technologies.

Here are the primary considerations to be taken into account for maximizing and governing data entry, extraction and management.

Assess the present Data Management setup

It is necessary to evaluate if the existing setup and systems are adequate to support the volumes of data being generated. Areas of concern include assessing current technologies in place and whether they are supporting required processed data output formats. If not, setbacks like stunted growth, adverse profitability and even enhanced data security risks are a real possibility.

If long term gains are to be made from digital transformation, all current data processing problems have to be left behind before new measures are put in place. A fully effective business digital platform will have to be devised that allows for multiple tiers of data mining and data discovery through applied intelligence for optimized business value. The critical factor is to get the maximum out of data, a worrying matter for most managers who feel that they are not getting the most from processed data. Hence it is imperative to first know whether existing technologies will support the new data solutions to be implemented.

Get the Most Out of Big Data

Most businesses have the capacity and infrastructure to handle huge volumes of relational data but are found wanting when it comes to unstructured or semi-structured data. In today’s digital age these have taken centre stage and are so large in volumes and so complex in their set-up that new tools and technologies are needed for their processing. Hence it is necessary that Data Integration techniques such as Data Streaming, Data Federation, ETL and Data Virtualization be implemented expeditiously. This will enable all companies and especially IT organizations to seamlessly blend and exchange on-premises data with Big Data from the Cloud. It will also enable optimized support and connectivity for working with large and different forms of data type.

Cleanse Data Using the Power of the Cloud and Self Service

Raw data is typically in corrupt or hard-to-use format. Hence companies should use systems that quickly and easily transform data in this state into a form that can be analyzed and processed quickly and effectively. For this to happen, systems should be installed that will help in data profiling, data enrichment, data blending and data cleansing. Only then will processed data be of some value to an organization. This is also one reason why Data Management services first cleanse all data before taking up processing to ensure its effectiveness.

The next point here is that companies should not apply one cleansing process to all data. Self service and evaluation of all data has to be carried out to know what requires to be “cleaned” before processing. Once done, it will help people who matter to have data in formats that will be useful to them. Hence, it is necessary to evaluate the current state of data before considering new technologies to migrate exiting data to a state of the art digital platform.

Enhance Information Administration

It is important that companies devise policies that will determine the way Data Management is done. Data entry and processing whether in-house or through Data Management services will then be able to offer outputs that preserve the value and integrity of data. This will by default, help improve quality of data processing, operational efficiencies and data administration initiatives.

A lack of accurate data can damage brand value of companies, causing financial losses. Companies will therefore do well to implement procedures for smooth transition to the modern digital age through optimized Data Management.

About the author

Avneet Narang works a Business Development Manager at Cogneesol, a well-renowned company offering Data Management, technology, accounting and legal services. She has been working with Cogneesol for the past 10 years and is responsible for generating sales for the Data Management division. While handling the projects of Data Management, she has witnessed a lot of changes over the years – how it all began from data entry, forms processing, data collection and slowly transitioned to data mining, data analysis and now Big Data. She has been thoroughly researching and sharing her viewpoints about these industry trends and changes on many platforms across the Internet.

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