big data implementation challenges

Supriya Nigam is a lead Content Writer & Digital Marketing Strategist at CareerBuilder India. Her passion for helping people in all aspects of digital marketing flows through in the expert HR Technology industry coverage she provides. Also, an avid Yoga practitioner.

While some companies are in dilemma of implementing big data, others are leaping into big data with a vengeance. HR executives already have enough on their plate, now the arrival of Big Data and disruptive HR tools scares them off. To overcome the big data implementation challenges require customised approach as there is no one-size-fits-all solution.

Nonetheless, the derailment of effectuating a plan of Big Data implementation, curtails from the mere fact that those organisations do not have the knowledge or resource to develop and implement the big data strategy on their own.

How big data does fit in Human Resource?

In HR, big data refers to the use of several data sources available to an organisation, including modern tools – such as cloud-based services, advanced analytics platforms and visualisation tools. Big data in HR metrics used to assess and improve practices including, talent acquisition, retention, and overall organisation performance.

It empowers the HR managers to analyse the tonnes of unstructured and structured data to answer significant questions concerning predictors of workforce attrition, workforce productivity, succession planning and impact of training programs on organisation performance.

Big data implementation challenges

big data implementation challenges
Img. Src. Business News Daily

Challenge #1 – Hadoop is tough

In one of the survey, “73% of respondents agreed that understanding the big data platform was the most noteworthy challenge of a big data project”.

Since the technology is comparatively new, several data professionals are not acquainted with how to manage Hadoop (concerning HR metrics).

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Solution – Increase the internal resources to maintain and train IT employees on advanced Hadoop courses.

Challenge #2 – Data Quality

Data warehouse is essential because data is coming from several different sources from all facets of the organisation. Keeping the every piece of data in its original form is affecting the data quality. Every year dirty data costs organisations $600 billion.

Some common reasons affecting the data quality includes incorrect data linking, duplicate data and input errors.

Solution – Being meticulous at cleaning and maintaining data and big data algorithms can be used to improve the data quality.

Challenge 3 # – Scalability

Most organisation fail to predict how quickly big data project can grow and evolve. However, it is crucial to be able to scale up and down on-demand with big data. Moreover, big data workloads tend to be a rupture, making it challenging to calculate where resources should be allocated.

Solution – As compared to on-premise solution implementation of HR analytics & big data plan on the cloud will scale much faster and easier.

Challenge #4 – Security

Another prominent big data implementation challenge is keeping that vast data sets secure. Particular hardships include:

  • Restricting access based on user’s requirement.
  • User authentication for every team member and team accessing the data.
  • Proper use of encryption on data at rest and in transit.
  • Meeting compliance regulations.
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Solution – Do not overlook the primary security measures, warrant that encryption integrates with access control and ensure proper enforcement and training.

Recommended big data implementation approach

Step 1 – Secure Executive level sponsorship

Big Data projects take the time to scope. Therefore, it needs to be proposed and fleshed out. Without the dedicated project team and executive support, there are fair chances it will fail.

Step 2 – Amplify rather than re-build

At this phase, try to receive approval to assess a few options until organisation settle on most appropriate technology for their HR metrics requirements. Therefore, one must start with existing data warehouses because here the challenge is – “ascertain and prioritise additional data sources and then identify the precise hub-and-spoke technology”.

Step 3 – Make value to the patron priority

Once an enterprise has acknowledged and prioritised the data sources. Now start connecting them to the requirements of the customer base.

Step 4 – First run a swift structure and increase over time

Start working on the incremental releases and integrate new data centres one at a time after establishing project team and priorities. Such approach will let organisation understand how to use data effectively to influence actions throughout the enterprise.

Step 5 – Connect customer data

Push data-driven decision throughout the enterprise – from the development of the product to pricing, packaging and promotion. Remember, each new set data represents prospect to change the way company deliver services and products.

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Step 6 – Develop repeatable process and action trails

Avoid “data paralysis” while taking a thoughtful methodology for integrating into data sets. Evaluate the responses from the learnings by asking team members what can be gained by adding data set. Do not just create another factoid devoid of a link to the product or the customer. Just clear the path for implementation with the organisation.

Step 7 – Trial, Quantify and Learn

Test the assumptions with each data set. If a company is using the big data appropriately, one can determine the most optimised solution to overcome big data implementation challenges right from recruitment to performance management to succession planning.

Step 8 – Map the data sets to the customer life cycle

At each stage of customer life cycle, begin mapping big data by asking following questions:

– How do customers discover new products or services?

– Can organisation connect that action to their advertising activities?

These 8 steps are primarily involved in the implementation of big data plan.

The mentioned above big data implementation challenges occur at all levels. Therefore, prior initiating the deployment of big data in HR metrics should be thoroughly thought out.

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