Working smart with big data: 6 things enterprises need to know

Big Data along with IoT and Machine Learning, is all already helping enterprises undergo huge transformation and disrupting industry verticals and horizontals. By now, it is safe to say that Big Data has arrived, although it is still early days.

As invincible as it seems, there’s still a chink in the Big Data armor of several enterprises. According to a report from Pricewaterhouse Coopers (PwC) and Iron Mountain, enterprises need to go the extra mile before using data to their advantage. The study included surveying 1,800 senior business leaders in North America and Europe, which have more than 250 employees along with companies with more than 2,500 employees. Among these, only a tiny percentage of firms said that they practiced effective data management.

The Google Flu Trends is an apt example, which shows the vulnerability of Big Data. When users searched for data on the search engine, its algorithm became susceptible to many terms, which were nowhere near the flu. The company didn’t even take into consideration the changes in the search behavior pattern of the users over time. It was a major flop show.

On that note, let’s learn the ropes of Big Data from those who’ve been there, done that and failed or from those who’ve succeeded in their endeavor and work smart.

1. Never Go Ahead Without a Strategy

Knowing that Big Data is a sure shot game changer, enterprises scurry to incorporate it in hook, line and sinker.  However, nothing will work until and unless businesses have an infallible enterprise Big Data analytics strategy in place. The strategy is required for providing a detailed blueprint of the organization’s potential strengths and risks for data-governance and data-handling capabilities. It helps them set their priorities with the existing data source, come up with effective data quality monitoring process, reduce the unrequited costs coming from redundant data and assess the data collected, including the value and the risks and do more.

2. Try Focused Training

Focused training is necessary for enterprises that know what they want to achieve with data analytics. For instance, Python has emerged as the most convenient language for data analysis and machine learning. The multi-domain, high-level, programming language is highly preferred for its easy readability, a vast array of libraries and reliability.

Hadoop and Python, combined together, help enterprises carry out complicated computation and data processing/analysis. Therefore, it makes sense for them to get their talent trained in machine learning with Python training or opt for other selective courses that’ll do the trick. The onus will be on them to analyze both, structured and non-structured data, and convert them into insights, which make sense for the enterprise.

This will definitely work for startups which may not have the capital to invest in a data scientist or highly-skilled personnel for handling Big Data. Nonetheless, such professionals are few and far between as it is a growing field.

3. Build a Robust IT Infrastructure

Big Data relies on advanced IT infrastructure, which includes data centers, cloud computing, faster servers etc. This allows businesses handle the constant flow of data coming from multiple sources and that too in huge capacities. If not handled well, it will put more pressure on the existing data centers. Also, more time and resources will be utilized to process the same.

4. Put Creativity in Action

The potential of Big Data goes beyond the conventional norms. Many enterprises fail to embrace its innovative side and succumb. Delta Air Lines Inc. for instance, set an example by using Big Data to simplify airline services.  It addressed a concern that plagues frequent flyers or passengers, which is ‘Lost Baggage’.

Using passenger data, the American airline came up with a solution by allowing passengers to track their baggage. Earlier, this would have been a painstaking process and need a lot of resources. However, with Big Data, the airline efficiently reduced mishandled baggage by 17% since 2007.

5. Make The Most of Consumer Behavior (In The Right Manner)

At the very crux of Big Data for enterprises lies consumer behavior analytics. It can add value for businesses and take it to new heights. Netflix, for instance, has a big repository of viewing habit of its users. With this knowledge, it creates and buys programming that can attract its existing user base.  The same holds true for other streaming services such as Amazon Prime and Hulu.

The plan can backfire also, such as that of Target, which used Big Data to predict a teen’s pregnancy (by analyzing her shopping behavior)! The retailer sent discount coupons for cribs and baby clothes and irked her father who had no clue about it. Although Target meant to use consumer data and push consumers to take a call-to-action, this episode led companies to think on the lines of privacy concerns.

6. Don’t Ignore Predictive Support

With big data comes big expectations! Businesses generally would want to tap into the potential of huge reserves of data with predictive models. Take the instance of a software called PredPol, which along with LA and Santa Kruz police departments, contributed significantly to reduce violent crime rates in LA. Originally meant for predicting earthquakes, the software was fed crime data and it could predict where crimes are most likely to occur (down to 500 square feet).


Big Data is an amalgamation of technology and innovation. Companies need to constantly strive towards updating their knowledge on using the influx of Big Data and its variety and use the same to their advantage.

Is your enterprise ready for Big Data? How do you plan to implement it smartly? Let us know in the comments!

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