What Is Big Data Analytics
Defining "Big Data Analytics"
Simply put, big data analytics is the process of obtaining large volumes of data and analyzing them for customer or competitor activity. Studying this data at scale allows you to eliminate short-term/fading consumer trends and short-lived competitive tactics. Big data analytics provides more significant insights that can be substantiated in digital interactions and then leveraged for competitive advantage.
Why Big Data Analytics Can Be Critical for Business
The importance of big data analytics is especially evident when collecting/researching open web data. It can shed light and lead to:
- Inclusion of a new product in the company's catalog based on obvious consumer demand.
- Changing a marketing campaign in line with target audience sentiment on social media.
- Improving/expanding the company's omnichannel retail experience based on competitor store architecture.
All these examples demonstrate how company-wide data collection and analysis can facilitate decision-making and yield useful and monetizable results.
What Does the Big Data Analytics Workflow Look Like
The big data analytics process is as follows:
1.Step one: Data collection from various sources
Web data from open sources can be collected in various formats (JSON, CSV, HTML) and from various sources, including:
- Social media
- E-commerce marketplaces (eBay, Amazon)
- Databases (government, investment)
2.Step two: Transformation, cleaning, and delivery of data
The transformation stage involves taking data in various formats and converting it into a unified format so it can be processed by target systems (for example, data collected in JSON, CSV, and HTML formats can be converted to Microsoft Excel to make it easier for decision-makers to evaluate trends).
This stage also includes "cleaning," which involves removing duplicate data points and values, as well as identifying and removing corrupted data files and file fields.
And finally, the formatted/cleaned data flows into the target system/storage/data pool/algorithm.
3.Step three: Data analytics and insight generation
The obtained data moves to the analytics stage, where one of the following methodologies may be used:
- Manual analytics: This can be performed by a team leader or fund manager who requests data, for example, in a Microsoft Excel sheet. They can simply review the volume of trades for a particular security, which helps determine, for instance, whether to buy or sell it.
- Natural language processing (NLP): This is when natural text and speech can be understood and processed by computers/machines. For example, a company analyzes competitor product reviews to identify ways to counteract them (e.g., dissatisfied customers can be attracted with faster delivery times).
- Algorithm training (AI/ML): Algorithms receive data both during the "training" phase and at the daily operational level. This initial/constant "feeding" allows algorithms to understand existing relationships or make educated assumptions based on large amounts of data (in a nutshell, data-driven AI/ML).
As you can see, data analysis quickly turns into generating insights. This often also lays the foundation for predictive analytics, which is the process of building behavioral models to forecast demand, trends, or consumer and competitor actions.
Tools and Technologies for Big Data Collection
Large-scale web scraping can be accomplished using a fully autonomous solution that leverages third-party resources. Essentially, this allows the first and second stages described earlier to be carried out, obtaining a large amount of industry data in real time for target "business consumers" (executives, algorithms).
All a company needs to do is:
- Step one: Choose a target website
- Step two: Determine delivery timelines and data format.
- Step three: Deliver the data directly to where it will be used for analytics (webhook, email, Amazon S3, Google Cloud, Microsoft Azure, SFTP, or API).
Applying Big Data Analytics for Competitive Advantage (Including Use Cases)
Let's look at five key examples of big data analytics that can help illustrate its use and application:
Big Data Analytics in E-commerce
Big data analytics can benefit digital commerce in a number of areas, including supply chain management. In this case, sellers can collect large volumes of data on competitors who have certain items "out of stock" and then sharply increase shipments of those items to attract impatient buyers.
Other eCom opportunities include using dynamic pricing models as well as optimizing shipping, warehousing, and supply routes. The former can be achieved by gathering information on industry-wide pricing, allowing prices to be raised or lowered to maximize conversion/profit.
The latter can be addressed by mapping locations where items are "ordered"/"shipped", allowing warehouses in target geographic locations to maintain approximate product volumes.
Big Data Analytics in Insurance
The insurance industry can gain significant advantages from using big data models in risk reduction. For example, analyzing crime rate data in the area where a company is applying for insurance coverage can shed light on potential risk as well as premium pricing. Historical weather data can also help calculate the likelihood of climate-related damage for a future insurance plan for a particular structure.
Big Data Analytics in Marketing
Marketing teams/agencies can benefit from big data analytics by understanding current search engine trends, analyzing competitor marketing campaigns, and analyzing target audience engagement on social media. For example, a shoe retailer could correlate the most trending search query ("where to find vintage sneakers?") with competitor ads promoting "pre-worn shoes" and high social media engagement for posts featuring such shoes. Each of these facts is "interesting" on its own, but at company scale, cross-referencing is no longer correlation but a concrete trend that can be exploited.
Big Data Analytics in Healthcare
Exercise data from wearables and fitness apps sheds light on what types of workouts people like, where they prefer to do them, and the top fitness challenges among different target audiences. Social media posts where people share their fitness achievements, such as "Hooray, I just ran 10 miles in Central Park," are a good source of relevant data. Enough such posts, and companies can conclude that park running is a major trend in New York and use that to promote medical products like knee braces for runners with damaged cartilage.
Big Data Analytics in Real Estate
Big data in real estate can bring significant benefits to both construction companies and real estate investment trusts (REITs). Construction companies can use analytics to predict price increases for building materials like lumber and concrete and stock up before price spikes.
REITs looking to participate in new real estate development plans can study current/future supply and demand, sales volume, as well as government documents and zoning committee plans (for infrastructure, public transportation, and parks) - all of which can contribute to making a property a profitable long-term investment.
Conclusion
Big data analytics can offer businesses a great deal in terms of real understanding of current target audiences and competitor trends. Done right, it can improve marketing, product development, sales processes, and reduce risks. However, to properly conduct big data analytics, it is necessary to ensure high-quality real-time data collection across the company to power analytical systems that can consistently produce results. As a result, many companies prefer to outsource data collection to focus on data analysis and practically implement the findings in the context of daily operations.