Data Management Strategy is not without its share of challenges; below we highlight seven that you may encounter during the development of your data management strategy.
Challenge #1: Insufficient Comprehension and Acknowledgment of Big Data
In many cases, organisations neglect to know even the pure basics of what big data is, its advantages, the foundations required, and so on. Without reasonable comprehension, a big data implementation project is destined to disappoint. Organisations may ultimately waste significant time and effort on things they don’t realise how to utilize.
Furthermore, if workers don’t see big data’s worth and additionally would prefer not to change the current procedures for its selection, they will oppose it and block the organisation’s advancement.
Challenge #2: Confusing Assortment of Big Data Technologies
It tends to be anything but difficult to lose all sense of direction in the assortment of huge information innovations now accessible available. Do you need Spark, or would the velocities of Hadoop MapReduce be sufficient? Is it better to store information in Cassandra or HBase? Finding appropriate responses can be overwhelming.
Challenge #3: Containing Expenditure
Big Data adoption projects can involve significant costs. If you adopt an on-premise approach, you’ll need to consider the expense of new equipment, new contracts (executives and engineers), data centre infrastructure, power, software packages, staff training, etc. Even though many of the required solutions may be open-source these days, you still need to consider the expense of advancement, arrangement, setup, and upkeep of new programming environments and tools.
On the off-chance that you settle on a cloud-based big data solution, many of the above expenses can be bundled into a monthly rental fee, the costs of cloud administrations and upkeep of required systems remains.
Regardless of an on-premise or cloud solution, you’ll have to take into consideration future extensions to maintain a strategic distance from big data development going astray and costing you a fortune.
Challenge #4: Multifaceted Nature of Overseeing Data Quality
Data from various sources: At some point or another, you’ll keep running into the issue of data integration since the information you have to investigate originates from assorted sources across a wide range of organisations. For example, eCommerce companies need to investigate information from web site logs, call-centres, contenders’ sites, and social media sources. Data formats will contrast, and coordinating them can be costly, time-consuming and risky.
Questionable data: No one is concealing the fact that big data isn’t 100% precise. And all things considered, data quality and accuracy are not simple tasks, but that doesn’t imply that you shouldn’t control how solid and consistent your information is. What’s more, data of a mediocre quality can’t bring helpful insights or knowledge to your highly demanding business undertakings.
Challenge #5: Converting Information Into Profitable Knowledge
Imagine a scenario where a store doesn’t stock the goods required by its customers because they weren’t able to derive the necessary information from point of sale systems or social media networks into profitable insights. As a result, the store loses the customer.
This scenario could result from big data tools not analysing sales history, and so stock management systems fail to update accordingly. Whereas, it is possible that rival stores had their store stocked with the same goods because his big data tool has noted the goods with the help of social media analysis in real-time.
Challenge #6: Big Data Security Lapses
Big data has its share of woes and security is one of them and big data security needs to consider both on-premise and cloud environments. Should a security incident occur, large volumes of data could be breached or leaked, which is a serious concern.
Challenge #7: Big Data Privacy in Data Storage
Storage of high-volumes of data may not be a big challenge because of cloud computing. But big data storage, if compromised, can disclose personally identifiable data with individual personal information exposed.