Big Data Success: 6 Steps to Achieving Super Growth Through Big Data

by Paul Shepherd
| September 26, 2018
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It is widely known that the exponential rise of social media use in everyday life, combined with the forever increasing amount of technology that people use for networking, will sustain as well as propel the enormous amounts of data being generated in a daily basis. Data that has a huge potential in helping businesses compete more effectively.

Nowadays big data has emerged as a leading opportunity for businesses that want to utilise every aspect of the ever-growing digital world. Big data success largely depends on knowing how to apply it effectively.

In this post, I will analyse what big data is and illustrate the steps involved in understanding and utilising it.

*Approx 8 minute read

Who Is This Post For?

  • Local business owners small to large
  • Multi-unit brands such as franchise groups, dealer networks and national brands with a local presence.

Commonly Asked Questions that I will Address:

  • What is big data
  • What are the 6 Steps to understanding big data

What is Big Data?

Big Data Success: 6 Steps to Achieving Super Growth Through Big Data

Big data refers to the procedure that is implemented when conventional data processing approaches are unable to clarify and segregate the important aspects of that fundamental data. Big data in most part is unstructured, vast and rapid and thus cannot be processed by standard database engines. This type of data demands a divergent processing approach illustrating the importance of it, as:

  • Advances in technology have allowed for much greater computational power, thus enabling more opportunities to process big data.
  • The Internet is steadily mass producing data across many platforms on a daily basis.

The 6 Steps to Big Data Success

We’re in a day and age where there is an infinite flow of data being collected from a vast amount of sources largely thanks to ample technological advances. Everything from websites, apps, emails, purchase transactions,  system logs, maps as well as location data — are being collected, processed and stored resulting in huge quantities of data.

Considering the massive amounts of data coming in, it can seem overwhelming to any business when trying to extract value from it. To delve further into this, here are steps illustrating the process to big data success.

1. Data Collection

Big data is a continuous data production cycle and collection is the first stage of that cycle. It can be said that this is the most significant step as the quality of data collected will have a snowball effect and influence the output greatly. The collection process must warrant that the data captured is both clarified and unambiguous, thus ensuring that all future decisions based on the results are grounded.

Examples of data collection include:

  • Loyalty Cards
  • Online Gaming
  • Satellite Data
  • Customer purchases
  • Customer demographics and commonalities
  • Browsing data
  • Site navigation analysis
  • Performance of all company marketing efforts

2. Data Mining

Once the data has been extracted and loaded into a database, it needs to be ‘mined’. Data mining is the process of determining relevant insights and meanings within that database. Through this process, companies are able to gain a better understanding of their customers and in turn initiate more effective strategies across most business functions.

In order to have a successful data mining process, the data collection and warehousing, as well as computer processing, has to be set up properly. For segmenting the data and evaluating the probability of future events, data mining uses sophisticated mathematical algorithms.

3. Data Cleaning

The combination of the Internet of Things (IoT) and social media have created untapped sources of data but not all this data is accurate.

Data cleaning is the process of eradicating polluted or inaccurate data. This process is necessary because false data can trigger wrong verdicts and decisions resulting in losses. This process allows businesses to structure the data and use it to focus on only relevant and valuable information. There various methods for cleaning data including:

  • Histograms
    Filtering the data through histograms can ascertain patterns and frequency of values that have a lower result, and could therefore, be invalid.
  • Conversion Tables
    This method can be used when particular data problems are overt and present.
  • Tools
    There are many companies such as Oracle, IBM, and Talend that supply data cleansing solutions.
  • Algorithms
    For some datasets there are several applicable algorithms. A given algorithm may be chosen because it works well for that certain size and/or nature of the data set. Algorithms can learn impressive insights from data, providing the dataset is cleaned.
  • Manually
    Currently, most data is generally cleaned manually. Even with the help of tools, histograms, and algorithms, human involvement is still necessary.

4. Data Consumption

As big data collection has increased exponentially, simultaneously the need for data consumption has grown more complex. Different users like consumer, administrator, business user, vendor, partner etc. will consume data in different formats. The output of analysis can be consumed in many ways including:

  • Businesses identifying retail trends in the market.
  • Government bodies targeting campaigns to reach out to a specific demographic.
  • Marketers targeting more effective marketing campaigns.

Big data is consumed at many places depending on the specific goals outlined.

5. Data Analysis

Data Analysis

Big Data Analytics is “the process of examining large data sets containing a variety of data types – i.e., Big Data – to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.”

There are several business benefits to Big Data Analytics:

  • More effective marketing campaigns and more informed business decisions
  • The discovery of new revenue opportunities
  • Improved customer service delivery
  • More efficient operations
  • Competitive advantage

Specifically, Big Data Analytics enables businesses to zoom into their data collected and look for the most relevant information and analyse it to make important business decisions.

Big Data Analytics has a transformative effect on business as it allows business managers and leaders to make decisions with detailed intelligence and insights available, often in real time.

6. Data Storing

A proper data storage system should encompass all the present day data analytics tools and storage space. Data can be stored on data storage providers like Cloudera, Hadoop and Talend.

The essential requirements of big data storage are that it can manage huge amounts of data, keep up with growth and provide the input/output operations per second (IOPS) necessary to deliver data to analytics tools.

Which type of storage you should use depends on the scale of your business.

Conclusion

There’s no doubt that big data will continue to play an important role in various different industries around the world. It can definitely do wonders for a business.

By understanding these 6 steps in the data analysis process, a business can make better decisions because the preferences are supported by data that has been vigorously collected and analysed. With time, the data analysis gets faster and more accurate resulting in businesses making much more effective decisions across all company levels.

In other words, the steps involved in processing big data give a structured understanding approach in terms of the at times overwhelming undertaking involved in managing vast amounts of big data.

To discuss the major benefits of using big data outlined specifically around your business model as well as specific solutions I have developed for Franchise groups and multi-unit organisations, contact me today for a confidential discussion.