search-close-icon

Search here

Can't find what you are looking for?

Feel free to get in touch with us for more information about our products and services.

Data Analysis: Five Steps to Superior Data

data analysis guide

This is one piece of a three-part series that looks at the various data analysis methods, techniques, and essential steps to ensure its superiority.


According to Wikipedia, data analysis is a process within data science of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful insights, informing conclusions, and supporting decision-making.

Data is crucial to making informed business decisions, but only if you have the right analysis tools and methods to transform a raw data set into actionable information.

Every day new data finds its way to the web on the scale of zettabytes (that’s 1021 bytes!). Most of it ends up in repositories where it gathers the metaphorical dust, never seeing the light of day.

Data is only as good as the insights derived from it.

Once you make your mind up, and decide to use data to bolster your decision-making process, you might be tempted to believe that half the battle is won. Far from it.

Since there is no universal way to perform this analysis owing to the ambiguity associated with the sourcing, analysis and interpretation of data, businesses must look at this process strategically, in detail.

Five steps to superior data analysis

steps involved in superior data analysis
Processes involved in superior analysis of data

Data analytics starts with a problem statement and ends with actionable insights. Each step is as valuable as the next. Failure to internalize one step may lead to the deterioration of the entire process. So, we recommend that you pay heed to every step with equal esteem.

Let’s go over the steps one at a time.

1. Define the objective

Before delving into the subject, formulate key questions that you are seeking the answers to.

  • Why has productivity taken a nosedive in my organization?
  • Is there a correlation between sales and brand reputation? If so, to what extent?
  • Are the customers looking for a particular kind of product?

Once you have adequately defined the problem statement, you can then begin to draft a working hypothesis that you can test along the way. Defining the objective will help you determine the sources you can extract data from, metrics you can measure, and the techniques you can use to run your analysis.

2. Procure the data

Once you define the pressing question, you can then move on to procuring the data. Considering the nature of the data sources will ultimately define the validity of your analysis, it is advisable to spend a good amount of time finding reliable sources.

Generally, data collection begins with internal sources, and moves on to external sources. Data collected from internal sources is gathered from within the company, whereas data collected from your clients and competitors (or any other source outside your organization) falls under external sources.

Internal sources:

  • Customer Relationship Management software
  • Internal databases
  • Sales Analysis reports
  • Enterprise Resource Planning software
  • And more…

External sources:

  • Google public data
  • Social media data
  • Government websites
  • Industry websites
  • Review websites
  • And more…

In the age of Big Data, it is becoming increasingly common to extract data from secondary sources to reinforce data analytics. While the decision to go beyond primary sources rests solely on your shoulders, doing so can take your analysis to a whole new level.

If you need to collect vast volumes of data from diverse sources on the internet, you can always avail our Concierge services, which will help you get all the data you need, hassle-free.

Data to make or break your business
Get high-priority web data for your business, when you want it.

3. Make sure that data is clean and structured

If your data is contaminated, you might as well stop the process right here, because everything you do hitherto will be nothing more than a zero-sum game, or worse!

We’ve repeated time and again the negative consequences bad data can have on your business. For, as is accepted worldwide —

Bad data is no better than no data.

Mel Netzhammer, Washington State University

It is not uncommon to find missing fields in a dataset. Not to mention faulty entries and outdated data. Structured data is easy for analysis software and data analysis tools to ingest, and the insights are much more valid.

At Grepsr, we use best industry practices, like data normalization, to ensure the integrity of your data. Moreover, our QA team, with its proven track record, implements strict guidelines to get rid of all the bad apples, and ensure that you have only the most accurate and structured data to work with.

4. Start analyzing the data

Once you’ve made sure that your data is clean, you can start analyzing it! You don’t need to be a data science wizard if you’re working with a relatively small amount of quantitative data. Common data analytics software and tools, such as Microsoft Excel, Tableau and Google Data Studio, are more than enough for statistical analysis.

But for more complex applications with various types of data, you can use the following techniques:

  • Regression analysis
  • Monte Carlo simulation
  • Predictive analysis
  • Prescriptive analysis
  • Fuzzy logic
  • Factor analysis
  • Sentiment Analysis
  • Cohort Analysis

Data analysts apply various kinds of analysis methods on your dataset to connect the dots in your strategy. Learn all about it here.

5. Share the insights

You’ve collected, cleaned, structured, and analyzed the data. Now it’s time to share it with the rest of the team, and interpret the results. This is when all the hard work you put into your analysis finally begins to bear fruit.

You can use data visualization tools to make it visually appealing, and easy to deduce. In other words, by using the power of data storytelling you can rally the entire team to achieve a particular goal with the insights gained from this exercise.

Keep in mind that you should not look to confirm your hypothesis, but accept whatever the result dictates.

Ask yourself the following questions:

  • Does the data answer the question you asked in the beginning? How?
  • Does the data enable you to make informed decisions? How?
  • Are there more perspectives yet to be considered?

If your findings stand strong against all these questions, you are most likely headed in the right direction.

This way, you can effectively utilize your structured data, whether it’s for a straightforward analysis, or complex machine learning applications.

To conclude

If there’s one thing you take away from this article it’d be to make sure that the data you collect is of the highest order. Then, as per your need, you can choose the best data analysis process to glean actionable insights. 

Data extraction, analysis, or visualization — the one thing common to all these terms is data, and you must work with the highest quality, from the get-go. For that, you can always count on Grepsr

Web data made accessible. At scale.
Tell us what you need. Let us ease your data sourcing pains!
BLOG

A collection of articles, announcements and updates from Grepsr

IMDb-Data-Thumbnail

IMDb Data Scraping: Turn Raw Entertainment into Actionable Insights

What if you could predict the next sleeper hit, build your own personalized recommendation engine, and forecast trending travel destinations? This isn’t science fiction. This is the power of IMDb data scraping.  IMDb is perhaps the most authoritative voice in movie and TV content for good reason — with 200+ million unique monthly visitors and […]

Benefits of Proactive Analytics

What is Proactive Analytics? How Netflix, Spotify, and Walmart Make Billions (2024)

Netflix, Spotify, Walmart, and other giants haven’t bet on their billion-dollar fortunes by shooting in the dark.  These companies’ proactive analytics allow them to curate hyper-targeted services that offer a core feature to their customers: personalization. The question is — are you still relying only on historical data to drive your business?  We’re living in […]

How to scrape blog posts

Blog Scraping: Uncover Opportunities for Data-Driven Growth

A study by HubSpot marketing shows that those businesses who publish blogs get 55% more website visitors, 77% more inbound links, and 434% more indexed pages than those who don’t.  The ultimate goal of any business is to continually increase its lead conversion rate. Content is essentially what leads the organization to bring more leads […]

Qualitative and Quantitative Data Analysis Methods

This is one piece of a three-part series that looks at the various methods, techniques, and essential steps to ensure superior data analysis. The majority of leaders from high-performing businesses attribute their success to data analytics. According to a survey done by McKinsey & Company, respondents from these companies are three times more likely to […]

Make Data Make Sense: Most-Used Techniques in Data Analysis

This is one piece of a three-part series that looks at the various methods, techniques, and essential steps to superior data analysis.

data analysis

Business Data Analytics — Why Enterprises Need It

Objectivity vs subjectivity The stories we hear as children have a way of mirroring the realities of everyday existence, unlike many things we experience as adults. An old folk tale from India is one of those stories. It goes something like this: A group of blind men goes to an elephant to find out its […]

data mining during covid

Role of Data Mining During the COVID-19 Outbreak

How web scraping and data mining can help predict, track and contain current and future disease outbreaks

Grepsr — the Numbers That Matter

Our stats since the start of 2018

web scraping with python

Track Changes in Your CSV Data Using Python and Pandas

So you’ve set up your online shop with your vendors’ data obtained via Grepsr’s extension, and you’re receiving their inventory listings as a CSV file regularly. Now you need to periodically monitor the data for changes on the vendors’ side — new additions, removals, price changes, etc. While your website automatically updates all this information when you […]

Data Analytics for Better Business Intelligence

Advanced information technology has brought a massive paradigm shift in every aspect of human life We spend more and more of our working hours on the digital screens, either generating or aggregating digital data. Internet, what would have seemed something unimaginable only a few decades ago, has become an essential part of our daily businesses. […]

Why Data Visualization Matters to Your Business

There are several reasons why we believe that visual representation of data is becoming an integral part of Big Data analytics or any other kind of data-driven analytics, for that matter

Location Analytics: ‘Where’ is the Knowledge of Data

Digital Technology and Rediscovery of Geography A substantial amount of data that Grepsr processes and provides to its business partners worldwide contains location-specific information. According to IDC, an American data research firm, 80% of data collected by organizations has location element, and according to ABI Research, location analytics market will rise up to $9 billion by […]

Data Mining: How Can Businesses Capitalize on Big Data?

In the recent years, data mining has become a prickly issue. The big controversies and clamors it has gathered in the political and business arenas suggest its importance in our time. No wonder, it is used as a household name in the business world. Data mining, in fact, is an inevitable consequence of all the technological innovations […]

Managed Data Extraction Service

Grepsr is what we like to call, “Managed Data Extraction Service”. Here are some of the reasons why we call it “managed”: We let you focus on your business and use the data — worrying about technical details of extraction is our job, and we will do it for you. We let you describe your […]

arrow-up-icon