Big Data is definitely changing the way how data is used in the organizations and large corporations. More and more organizations have started concentrating on big data as they have realized the potential of the new technology. Big data helps in understanding the preferences of the customers and help us recognize trends and patterns in the market.
However, the most affordable “off-the-shelf” data analytics tools mostly provide partial perspectives to a business or have a limited scope of functions. This potentially means that any business which may want a more holistic overview of their progress will need to procure a bevy of different tools and spend a large amount of time correlating results. Ironically, businesses like SMEs are almost always strapped for time and resource because of their constant focus on the bottom-line.
Also, data security is also another real-world challenge today. Hence DATAVLT came up with a solution to solve some of the key issues that the industry is facing and will face in the future.
ISSUES THAT DATAVLT AIMS TO RESOLVE
Technological Developments: Data analytic capabilities mean the need for superior computing requirements simply because of its volume and processing intensity. This is a challenge in terms of processing cost because backed infrastructures are not easily affordable.
DATAVLT has created a platform that can be used without the need for a separate infrastructure.
Funding: Organizations with the aim of implementing big data would require heavy investments which mean a high probability of skewing original intentions for data analytics to be replaced by faster go-to-market and ROI strategies.
DATAVLT provides an affordable solution as it does not require a separate entity at the site.
Talent Pool: As the need for big data increases, the number of data science talents also increases. However, in reality, the number of data scientists is limited and most would have hired by large corporations to manage internal business intelligence requirements.
DATAVLT ensures that there is no necessity for a separate team of data scientist.
