updated - January 28, 2020 Tuesday EST
Hadoop is everyone's favorite choice whenever big data platforms are brought up. Its creation was revolutionary because it introduced methods of storing, processing and retrieving data that previously didn't exist in the realm of computing. Hadoop also helped to address emerging concerns in the recently-discovered field of big data, such as storing, securing, and querying large sets of data.
With more data available to the average business today than at any other point in history, a lot of these concerns remain valid, and have, in many respects, been exponentiated. Hadoop remains an important piece of technology in the domain of big data and can be of great benefit to businesses today.
Big data provides companies access to information that may prove crucial in creating a competitive advantage over competitors. Competitive advantage may come in many forms, depending on how cut-through the other players in your market segment are.
One such advantage is introducing the concept of buyer personas into your business' workflow. The creation of buyer personas didn't start with the rise of big data. Most marketers create some form of buyer persona intuitively.
For instance, for a traditional brick-and-mortar retailer selling shoes, the ideal customer might be a 23-35-year-old middle-class male with no children. Big data takes the concept of buyer personas further - providing more detail to work with.
Sophisticated data analytics can also be used within the company to improve decision-making, uncover previously-unknown insights and minimize the risk taken on. This can be thought of as the same way traditional retailers can offer products on credit to customers they are familiar with and trust.
One of the most revolutionary concepts introduced by the continual availability of big data is buyer personas. A buyer persona is a virtual representation of who your site visitors and customers are. Characteristics that can be represented in a buyer persona include age, gender, marital status, income level, and employment status.
Personas can further be made accurate through the inclusion of non-identifiable personal information such as interests, location, past purchasing history, and how they interact with your company. These help to add context to personal information.
For instance, by knowing the geographic location, a business can gain knowledge into the culture that surrounds a persona. Such information may have profound effects on marketing campaigns.
Buyer personas are the basic building blocks of targeted advertising. When well-implemented, they should tell you why users visit your website, what they think about your brand and how likely they are to convert from being prospects to actual buyers.
More detailed personas can often provide information such as a customer's lifetime value, actions they are likely to take in the future and what kind of content they respond best to. The more detailed they are, the better the customer's experience, and the better the leads you can generate.
Big data enables better experimentation and testing. Before launching a new product, it's routine for companies to test their viability in different markets. Introducing a product or service market segment at a time ensures there's as little friction as possible in its adoption. This can be done in one of two major ways.
The first is by exploiting buyer personas. A company could take the personalization approach and test a product with a few customers who have buyer personas that are more or less similar. Depending on the sentiment these customers have to the product or feature, the business can predict what the general sentiment around it will be with the greater population.
The second option that big data provides is the more traditional market segmentation proposition. It's a lot more common to see companies testing this because it usually involves whole communities or countries.
For example, Facebook is known to have tested their new dating feature in Malaysia and other parts of the continent before it went live. This approach is normally adopted when the number of customers is extremely large, and formulating a single buyer persona that encapsulates all of them is impossible.
Both approaches are advantageous because businesses can create products that resonate with the target audience.
Before the availability of big data analytics software like Hadoop, companies had to wait for days to weeks before actionable insights could be gleaned from processed data. On the back of cheaper hardware, faster processing, and more advanced software, new technology such as machine learning have significantly shortened the delay.
A great example of this is in-memory processing. RAM prices have dropped from $0.112 per MB in 2006 when the MapReduce paper that powers Hadoop was first released to $0.0027 per MB in 2019.
This marked drop has enabled the onset of faster processing by storing and processing data inside a computer's RAM, which is 100 times faster than disk-based processing like traditional RDBMS implement. This apparent speed with the trade-off for volatility and extra expense is one of the factors that has driven the great Hadoop vs Spark debate.
Big data enables companies to better study and understand current market conditions and trends. Trends are what allow investors and traders to make a profit. They can be measured within a short or long timeframe and are used to judge how profitable various ventures undertaken by businesses are likely to be.
They are affected by factors such as government initiatives, international markets, supply and demand, and market expectations.
Big data analysis enables businesses to carry out sentiment analysis. The general sentiment around the actions of a brand, the state of the economy and the availability of cash flow are all crucial to ensure businesses can turn a profit at the end of the fiscal year.
Big data can create several new growth opportunities for businesses, and even help to discover whole new categories and products that reverberate with the business. The presence or lack of big data analytics in a company will also make a world of difference in how marketing campaigns are planned and executed. Companies with the necessary resources should aggressively pursue the adoption of analytics applications in their business.
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