Big data: The New Currency In 21st Century Enterprise

As data-driven marketers, we’re constantly looking for new ways to data-mine information from everything to candidate research to mailing lists. The Internet has effectively become a marketing treasure trove for big data. Yet with Privacy Awareness Week currently on, I’d thought to share my two cents on big data in the information age and give you a run down on how your data is stored for safeguarding by enterprise.

Generally speaking, we don’t completely trust technology and the Cloud, especially while the current cyber climate is still working out the kinks in privacy protection legislation. The mentality everyone should adopt is if it’s online, anyone can access it. This lends to a number of issues for privacy and data security especially as organisations look to secure agile data transfer in the CRM system.

The Goal Toward Agile Big Data Transfer

All larger enterprises still struggle with agile, fool proof methods of transferring sensitive data across all levels of the organisation and cross-institutionally. You can be sure that internal and external communication limitations are at the core of on-going analysis and research for improvement and if not, then falling that much further behind the competition.

Public sector organisations also understand they are no match for heavily funded private sector organisations in terms of securing processes and data. However with a combination of generic measures for asset protection and strategically placed expertise plus a large number of labour, customised methods for data transfer act as a buffer against international conglomerates knowing all where sufficient funds can facilitate data exchange i.e Private sector firms such as Google which have billions of liquid assets as opposed to public sector debt. The general outcome being that these organisations are still in for a shot at ‘fair play’.

How Is Your Data Stored?

Data Warehouse Process

Typically, big data is automated via multiple business units (“MBUs”) for ease of distribution and theoretically, added security. Independent MBUs in the enterprise architect all act to contain data via customised industry specific languages. Diversification by segregated MBUs follow a holistic and semi-transparent model, which act independently to store data, grouping only when necessary. Essentially multiple business units is sound by not having “all your eggs in one basket” whilst simultaneously banking on the fact that the competition will not have the resources (labour, technological, technical) to follow multiple leads. For your added security, despite being able to gain valid short-term data pending a security breach, long-term data is skewed.

Yet automation is often too systematic to be agile. If we’re banking on lack of human error, which is so intensively and intrinsically adapted in daily processes over an extended period of time but without mental and emotional triggers taking a toll on room for human error, the process remains fundamentally flawed, especially where most reported data breaches involve accidental loss or release of data. Given over stimulation via brute force attacks (both IRL and otherwise) once a vulnerable port is identified within the system, thus lending to interpreted data inclusive of manipulated error passed forward along the supply chain, ‘the ripple effect’ creates communication limitations in the overall eco-system.

Going Back To Basics?

Generally, for any end user, we don’t need to become experts as such at enterprise architect to know ‘just enough’ to get the gist of understanding the problem via generic terms. Take education for example who by no means have the technological means or access for state-of-the-art technological security as with any organisation operating on publicly accessible databases, public Wi-Fi and open VPNs, the fact remains that no level of funding, ahead of the majority technological access or technical training is fool-proof. Big data automation via digital migration is also much more difficult to implement in terms of training new staff, risk-adversity and time required to make across the board changes.

From a view of safeguarding our data online, the key for small to medium enterprise still lies in simplicity. Or at least as simple as it can get in the digital sphere. IKEA’s parody on Apple made an excellent creative point on getting back to the basics as a comparative advantage through a non-direct compete. The logic behind this is that there is innovation by having data hide in plain site through paper-based processes lending to at least part of the CRM being removed from an otherwise flawed system of big data automation. Similar to batch integration, partial integration of paper based processes also gives time for data to be verified.

Considering your phone can already be used to track your location, Facebook to see where you frequent, that TVSN shopping deal you signed up for last week has your address, and your personal data only sells for $8 a month to have access to your social media accounts and feed of transactions from your credit or debit card, end-users are already left with a lot to worry about without fretting over organisational mishaps especially where data is outsourced through agent-assisted data centres.

Going back to basics is a measure we can all take in SME to overcome big data flaws, especially where there are security limitations when technological advances can’t quite keep up with human error and theft. Albeit a setback for digital migration, it’s still one step forward for data security when integrated paper based processes are implemented together with real-time technological databases for added security.

For SME, what’s the cost-benefit of data security upgrades and automation compared to the cost of labour and time needed for paper-based integration? Is customer data outsourced to agent-assisted data centres secure and worth the poorer consumer perception of having to share information with an additional third party?


Originally published at www.inkth.com.