What are the first line challenges today before enterprises with respect to online data management?
- Proliferation of data
- Management of big data
- Contextual and result oriented analysis of data
- Pragmatic application of data findings
- Tools and applications relevant in compounding data management
1) Proliferation of data:
The size of the very first hard disk I used was 8MB. As a physical object the size was quite big whereas the storage capacity was very small. In today’s terms it just can accommodate one or two songs. In quite contrast to that we have today a thumb size and even smaller storage chips that accommodate gigabytes of information.
The moot point in the context is proliferating data. Any single person today has several storage spaces for his data like (i) data stored on mobile phone, (ii) data placed on Tablet PC, (iii) data produced on Laptop, (iv) data manufactured on PC (v) data collaborated using Social Media tools and (vi) data on the cloud. Again we have official data and personal data. All these data sources are only contributing to more data, redundant data or data proliferation.
2) Data Management:
Irrespective of job position each and every employee in an enterprise adds some amount of data every day to enterprise data repository. Here comes the challenge to manage it through analysis, verification and validation.
Data management involves (a) right inputs (b) controlled sharing (c) structured storage (d) secured storage (e) appropriate tools for analysis, verification and validation and (f) contextual and discriminatory retrieval.
If the data is not rightly managed going by the above parameters we add more waste data and compound the data management problems. The end result will be chaotic data management, improper decisions, and a little or nil performance.
3) Contextual and result oriented analysis of data:
This is one of the biggest challenges in data management. I would like to cite an example taking Google Analytics data. Google Analytics gathers and displays highly comprehensive data related to an Internet site. The parameters are endless and beyond the one shown in the image below.
From the data set ‘Behaviour’ we can gather information about how many new and repeat visitors have come to our site. What is the frequency and recency of their visits? What is the duration of each visit and how many pages they viewed?
Proper analysis of above data set would help us better our pages and its content. It would also help us understand site loyalty. As a whole we can achieve greater marketing ROI if we can analytically review all the data sets and make appropriate changes to our Internet site pages. Result oriented analysis can be done comparing the original version of site and changed version (with respect to the content, design, object placement, feature facilitation) of the site pages.
4) Pragmatic application of data findings:
Say we have set the page “download evaluation copy” as a landing page. There have been 1000 downloads on an average per month. If we can compare the number of downloads and the number of actual purchases with the sources of download and sources of purchase, it is possible for us to find out the performance of the landing page and product acceptance. Based on such data we can fine tune the site page(s) to respective demography and position our marketing and sales pitch through additional publicity, deployment of sales team, social media marketing, road shows and any other viable means of promotion.
So unless we apply the data findings for a proper use, proliferation of all this data would only add to our confusion or totally goes in vain.
5) Tools and applications relevant in compounding online data management:
We have several tools from all the big IT MNCs for offline data management including the very ERP applications that come with Enterprise Data Analysis modules.
However, considering the rapid adoption of online marketing means and social media engagement it is imperative for us to explore software tools and applications that can gather, analyze, and present us required information from our social media presence. Be it data gathered from our Facebook pages, data gathered from Twitter engagement or from any other online profile.
You may explore the relevance of following sources to your context: