5 Ways to Accelerate Data Wrangling

Data is gold for marketers. Research states that by 2020 consumers will self-manage 85% of their brand relationships without assistance. Also, insights will be the primary source of these decisions.

In light of this, the importance of data cannot be undermined. But not all data is worthy. In fact, sifting through thousands of data sets to get accurate data might not be possible, every time.

But as a marketer and a data expert you need not spend a lot of time on data analysis. Data wrangling is one technique that hastens the pace to get valuable insights.

Five ways to speed up Data Wrangling are:

1. Keep yourself updated by maintaining a journal/data log

Siloed data sets across different spectrum make it impossible to move through the right data sets.

In a given timeframe, it becomes difficult to pinpoint exactly the right solutions to pressing issues. It might also need immediate results. The best way to approach these sudden and urgent problems are:

  • Keep track of all your experiences, problems faced and solutions to refer to it
  • Identify the best approach to each problem and its solution
  • Set 4-5 parameters that were deemed high priority and map how they were resolved

Keeping a log of activities and problem-solution is a simple process. It can quicken the time spent on taking the right approach. It can also reduce the query resolution time.

2. Be a part of communities and read up on case studies in your industry.

Often the best way to learn is to find out how your peers and colleagues are performing. Joining communities and groups that have common interests can fasten your pace of learning. You can also get familiar with people who are also trying to build their careers in data science by helping them and learning in the process.

It also brings along with many experiences and case studies. These can be an eye-opener.

3. Collaborate with other teams

Every team in the organization has its own goals and objectives. But, most often they are tied together with one common organizational goal.

Collaborating with different teams like engineering, data scientists and also inter-departmental teams can be beneficial. It can give rise to different viewpoints. It can help understand the problem from a different perspective.

For e.g. the need to understand customer issues might be a product development idea by the marketing team, but for the operations team, it can mean reducing the time spent on logistics.

Collaborating can thus fasten the pace of finding the right data sets. It can also bring together the best ideas. It can be innovative and hence provide a comprehensive view of the data sets to be worked upon.

4. Keep an eye on the end goal

If data wrangling is used to gather customer insights from many channels like social media, questionnaire, feedback forms, etc., then it becomes easier to know where the data will be used, i.e. customer retention or loyalty.

However, on the other hand, if the end use of data wrangling is not known then it becomes difficult. It can be a long way from finding out the actual outcome desired out of data wrangling.

Thus identify the end goals desired from data wrangling and in turn fasten the process.

5. Lean on smart insights that are actionable

Smart insight can be extracted when data wrangling answers few fundamental questions like:

  • Can it ensure scalability and granularity?
  • How can the same data sets be used across different queries and different time spans
  • Can tools and techniques reduce the time spent on data wrangling?
  • Can the retrieved data be put into practice immediately with minimal variations?

Answering the above questions can receive actionable insights that can fasten data wrangling

Closing Thoughts

Data wrangling can be fastened by following the above steps. It can also be improved by learning from others in your field. Learning together can usher in new tools and techniques. It can fasten the pace of data wrangling.

Have you got any quick data wrangling techniques? Share it with us in the comments!

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