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With millions of pieces of content emanating in various forms, sizes, and shapes across multiple sources, it becomes a challenge to choose a needle in the information haystack and utilize it for organizational benefit in decision making and strategy building. However, that doesn’t mean that it is not at all possible. Keeping a few pointers in mind in what not to do when harvesting and using big data will go a long way in helping you to use this information goldmine to serve your growth ambitions. Avoiding these issues will help you embrace big data wholeheartedly into your systems.

Understanding the significance of big data

Many companies equate big data with huge mounds of information gathering. However, big data is not all about quantity. Having a tactical approach to big data extraction services, as well as carrying out the right data analytics will determine how successful big data implementation in your company is.

If you are an entrepreneur or business analyst, your job will be to study these data sets, perform a critical analysis on them, and present visualization to executive leadership to help them plan strategic moves. You need to keep in mind that your findings must enable factors such as:

  •    Cost reductions
  •    New product development
  •    Time reductions
  •    Judicious decision-making

4b-Why are companies finding big data too hot to handle

Utilizing the prowess of data analytics will help you accomplish quite a few crucial things:

  1. Performing researches on customer behavior and generating specific marketing campaigns, thus increasing effectiveness amongst these clients. This is precisely what happens when companies crawl travel reviews or crawl hotel prices.
  2. Finding the prime causes responsible for real-time failures using Business intelligence.
  3. Enhancing efficiencies within operational and decision support processes within companies

Types of big data and their analysis

Before comprehending the characteristic features of handling big data, it’s good to identify the sources of its origin. As of now, big data is classified into three categories.

  • Streaming data: These particular data sets originate from a network of connected devices and reach to the IT systems in your enterprises
  • Public sources: Open or public data sources also account for a majority of your big-data set. A great degree of volume and velocity emanates from these sources
  • Social-media data: Data achieved from social interactions around your brand happens to be of paramount importance for sales, marketing, and other support functions. Although it is a significant challenge to consume and analyze this kind of data.

Significance of big data

Deploying systems, processes, and tools around big data offer the golden opportunity of analyzing valuable business data sets and gaining actionable insights that give direction to the senior leadership’s strategic moves in business.

Some of the crucial factors that CIOs and data teams need to have in place to make big data work include –

  •    Faster processors to compute millions of queries through billions of records
  •    Value for money and abundant storage
  •    Parallel processing, virtualization, clustering, MPP, high connectivity, high throughputs, and large-grid environments.
  •    Using affordable, distributed, and open-source big data platforms.
  •    Flexible resource dispensation arrangements and cloud computing.
  •    On top of it all, sufficient human talent that has a knack for extracting meaningful information from seemingly random numbers.

Grave errors involving big data planning and execution

For successful big-data implementation, you need to have a professional approach. It is here that enterprises need to take additional care and steer clear of committing mistakes. And this will be your key to attaining better analytical insights. Ignore these mistakes and you too will find big data too hot to handle. Some of these grave errors are:

  • Comprehending data relevance

Big data can come in various sizes and shapes. It is here that you need to comprehend the relevance of these data sets in compliance with your specific business requirements. Today, big data can be:

  •   Unstructured data including audio, videos, text, and images.
  •   Semi-structured data that includes earning reports, software modules, spreadsheets, and emails.
  •   Structured data sets encompassing machine data, actuarial models, financial models, sensor data, risk models, and mathematical model outputs.

For potential enterprises, it is not just enough to gain access to these data sets. It is also important to understand their relevance to enterprise analytics. So while there will be millions and millions of pieces of data available, data scientists will need to focus only on those chunks that are valuable to your company.  

  • Undermining data quality

In big data projects, impaired data quality in the data warehouse can inflict fatal blows on business analytics. Therefore, the quality of the data collected during data mining plays a huge part in the value provided at the final insight backed decision-making stage. While performing big-data incorporation, you must take the utmost care in integrating semi-structured or unstructured data into existing business data sets. That can be an important step towards the degradation of data quality.

Therefore, you must never underestimate data quality and take the most appropriate steps to resolve any data issues before processing it.

4a-Why are companies finding big data too hot to handle

  • Improper data contextualization

Textual data processing and perfect execution of text analytics require a fundamental logic known as data contextualization. Your failure to contextualize data can lead to inaccurate data processing prone to costly insight mishaps. And that will inevitably lead to erroneous analytics.

You need to be on your toes while performing data contextualization and improving data quality. Undertaking other significant steps such as text analytics categorization and homographs will be the smartest move towards enhancing data quality.

  • Lack of proper business case

While integrating big data in the decision-support platform of your enterprise, there is no denying the importance and need of an appropriate business case. Suppose, yours is a logistics firm, and you wish to use social media data to comprehend customer expectations and perform brand monitoring. While doing so, you will require including numerous variables in your business case such as market analysis, geospatial information regarding consumers, and competitive brand study. And all these constituents together will take your entrepreneurial venture to the peak of success. Therefore, a proper business case is always of paramount importance.

  • Ignoring or avoiding data preparation

Before the processing of big data, it is essential to prepare it. Moreover, there is also a need for providing additional inputs, as and when required for taxonomies and metadata. On the majority of occasions, organizations ignore the data preparation steps leading to improper processing of the same. Not only does it wreak havoc on the entire process of data processing, but it also limits the full functioning power of big data users and operators.

Importance of error identification

Identifying and unraveling these errors at the very onset of data analysis projects will prove to be highly beneficial. Essential steps of mitigation can be taken much before the issues get aggravated, and adopt a gigantic shape. A detailed study of big data sets and careful learning can help identify these errors and mitigate them at the very onset.

What can be done?

Setting up a concrete insights generating strategy can be viewed as a viable solution. Right from knowing what questions and issues to pursue, to acting upon the insights gained from big data analytics, every step needs to have a logical beginning and end and work towards a greater goal as pre-determined by your company.

So to start, the leadership may ask: Why are we losing sales in France (an open-ended question) or how can we target 25-40-year-old males over the next 2 years to adopt our new range of leather footwear (a more refined query).

Once the data scientists explore data within the closed-end loop, they can propose systems, techniques, and processes that will aid the different divisions within the company to work towards fulfilling their objective. For this, they need to propose hypotheses, formulate a way of testing multiple hypotheses against each other and then see what works best. Also, the finalized hypothesis will then need to undergo multiple iterations of fine-tuning and re-testing to ensure that the latest results match the initial findings (i.e. no disruptions damage the viability of the finalized hypothesis. For this, they need data, more data, and even more data – a.k.a. Big Data.

Once the data is in place, BI tools and visualization software help uncover interesting observations from the data, through charts, reports, dashboards. This dissemination of information helps the relevant stakeholders to devise a course of action that will help them meet their stated objectives.

So for our second example above, the marketing team and product development team get meaningful insights and see what size, shape color, material preferences do these niche groups bother about? This way they can better design their next steps of action, keeping these pearls of knowledge in mind.  

Concluding note

Big-data proves to be highly beneficial in numerous projects. Avid travelers can crawl travel reviews and discover the actual costs of traveling to those desired destinations. Additionally, they also have the option to crawl hotel prices and find out the most convenient rates. However irrespective of the use case, it remains a universal fact that only when data is strategically collected and properly analyzed, will it serve the purpose.

Do write in to us and let us know if you too need the power of Big Data to provide the much-needed growth thrust to your business from expert eyes.

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