Big data is one major modern innovation that embodies accuracy, rapidity, extent and approach to various internal and external data sources and principles that sometimes maybe impractical to information security practices. As big data analysis evolves, companies and industries are assigning private data storage units to third party technical specialists and data scientists.These are some of the practices and suggestions that keep industrial and company big data programs updated, accurate and secure.
Table of Contents
Data is always valuable as the bits and bytes transferred through database applications and running on special hardware command high value in the market. Is data as the most valuable aspect of any big data project that makes it unique and central in achieving set goals? This data may contain special copyright or proprietary insights into any customer base or an intimate look at the financial health of any company or industry.
It is improbable to leave expensive new disk enclosures powering industry or company analytics servers without manual supervision outside the company premises. It is vital to keep a close watch over delicate company data by creating policies and procedures that protect and safeguard valuable assets.
It does matter that even though businesses, companies and industries have the best and most reliable big data protection services in place, to ensure data vendors don’t poorly handle or even exchange sensitive data with other third parties, or mismanage this valuable asset. If this happens, the protection may prove futile, aimless and costly in potential legal liability. It is better rather than relying on assurances, to insist and enforce that vendors agree to minimum acceptable standards attached with a penalty for violating the accepted standards. It is good practice to check training manuals and data security compliance statistics before handing over sensitive data. If unable to vet vendors acceptably, having sample data containing similar fields as the genuine datasets and a diversity of realistic data not containing actual data or identifiers is a better choice. This is adapted to make and vet analytical model or approach applicable to true data in realistic managed situations.
What is vital as the raw data that feeds analytics engines is the result. Big data gives actionable decisions or information that no other company. It’s possible to find markets and factors that give businesses or companies an edge over competitors or catch signs of future market states. It’s good to ensure outcomes of big data projects are safe and trailed vigilantly like initial data.
With big data usage growing, the practices applied in big data are adapted for speed than security. For new big data projects, it entices to use new open source analytic engines, load sample tables of financial or customer data and see potential results before anxiety on data security, proper security and access control.
It’s advisable to have basic practices in place alongside big data tools and know who can access the data and the underlying financial implications in case data is revealed. Securing big data is a fundamental principle and critical point that high-value companies and industries should take with a seasoned approach.
