Infoxication: Why Big Data is the solution
Infoxication is one of the diseases of the 21st century, caused by the overload of information generated by the digital world.
It was the Spaniard Alfons Cornella, a technology expert and best-selling author, who gave rise to the concept there by the early 2000s. He began to analyze the situation caused by the immense amount of accessible content and concluded that it is a disease, yes.
Yes, the term is new and seems a bit exotic, but that’s right, individuals and societies are in a time when excess information is generating intoxication.
Here, let’s reflect on Infoxication at the business level, which has to do with the concept of Big Data, as we will see throughout this article.
Find out how your business can take advantage of this phenomenon and how to deal with Big Data in a profitable way and more!
Infoxication and Big Data: Business in the Data Age
In recent years, we have faced the concept of Big Data, widely present in the agendas of managers – there are several experts tying Alfons Cornella’s Infoxication with this phenomenon.
Let’s go further: Big Data is the way out to circumvent the intoxication of information and data that people, and businesses are living through.
Let’s remember quickly: Big Data, in the corporate world, is the term that summarizes the scenario of high data volume, high speed and assets of information of immense variety that require innovative and cost-effective forms of processing to increase profitability, improve the perception, decision-making and automation of processes. Taking control of the exponential volume of data is more efficient than simply ignoring it.
The internet, which is one of the most transformative innovations of the modern era, was the catalyst for the emergence of Infoxication. Consumers take advantage of connectivity for purposes such as entertainment, education, knowledge, social sharing and shopping. Brands no longer control information about their products and services. Instead, informed users enter business transactions with information about what they want and what they should pay.
The idea of those who argue that a good Big Data strategy can prevent Infoxication in business is more or less this: an explosion of consumer data, rather than scaring a brand, should allow it to form targeted relationships more easily, offering personalized experiences.
Big Data Strategy: How to Deal With Infoxication in Business
As you saw, the problem is a given. It cannot be ignored, it may be that competition is taking very good care of your data. You have to take the reins of Big Data and look for a strategy.
In very practical terms, a Big Data strategy is nothing more than a set of structured actions to handle internal and external data—understanding it, capturing, ordering, analyzing, and transforming that data into business-useful information and insights.
A Big Data strategy comprises the creation of processes, the definition of objectives and the use of data analysis tools and services. You should also understand the necessity of the acquisition of professionals (data scientists, analysts, consultants, among others) or specialized companies (which have prepared teams) to operationalize the strategy.
The goal is to transform the data ecosystem that surrounds your company into a competitive differential for the evolution of your business. In other words, differentiate yourself in the market, create disruptive products and services, satisfy the consumer and get to know the competition better.
Start now: 5 tips to build your Big Data Strategy
There is no ready-made formula for creating a Big Data strategy. This is because it is necessary to understand several variables in each business. What exists are some initial guidelines that can be internalized by companies that are taking the first steps. And these are the ones we’re going to show. Check it out!
1. Set a goal for your Big Data strategy
Its ultimate goal is the most important information for planning.
What do you want the data to contain?
You need to decide whether to increase the efficiency of customer representatives, improve operational efficiency, increase revenues, provide a better customer experience, or improve marketing.
The goals you have should be accurate, correct and straightforward. Any strategy for the sole purpose of exploring possibilities is likely to end up in confusion.
Based on your goal, you can choose a methodology, hire employees, and integrate the right sources of data.
Remember: Create SMART goals (specific, measurable, attainable, relevant, and timely goals) and make plans according to those goals.
2. Determine the economic value of your data
In a Big Data strategy, it is critical to link a financial value of objectives to conversion funnels (variables and metrics). Thus, you can monitor the return on investment and convince stakeholders of the relevance of your data.
3. Choose a Big Data approach that adheres to your goal
Let’s show you four ways to create a Big Data strategy.
Based on your data objective and availability, choose one of them:
- Performance Management: Involves the use of transactional data, such as customer purchase history, turnover, and inventory levels, to make decisions related to store administration and operational supremacy.
Tip: Works well with companies with large historical databases that can be harnessed without much effort. It can also help with better targeting and acquiring customers.
- Data Exploration: This approach makes intense use of data mining and research to find solutions and correlations that are not easily detectable with internal data.
Tip: Today, it’s an approach used by companies focused on robust Inbound Marketing to generate insights into the behavior of prospects on digital channels. It helps you identify new data segments and bring insights into customer behavior and preferences.
- Social Analytics: Social analytics measures non-transactional data across multiple social media platforms and reviews sites like Facebook, Twitter and Google+. It is based on the analysis of conversations and comments that arise on these platforms. It brings three primary analyses: awareness, engagement and word-of-mouth. In-stream data analysis techniques, such as sentiment analysis, are very effective in these cases.
Tip: Provides insights into brand identity and customer opinions about new offerings and services. It is also effective in predicting peak demand for certain products.
- Decision Science: refers to experiments and analysis of non-transactional data, such as consumer-generated content, ideas and reviews. Decision science is more about exploring possibilities than measuring known goals. Unlike social analysis, which is based on engagement analysis, decision science focuses on hypothesis testing and the idea generation process.
Tip: Involves extensive use of text and sentiment analysis to understand customer opinions about new services and schemes.
4. Discuss and prioritize data sources
This step focuses on brainstorming and prioritizing the different data sources you have.
Because Data Science refers to “identifying variables and metrics that can be better predictors of operational or business performance,” it is important to have a process where stakeholders can collaborate with the data science team to identify and test different data sources to identify those that can produce the best predictive models.
5. Invest in specialized tools and services
Finally, it is essential to invest in tools and services specialized in Big Data.
The good news is that it is currently quite cheap and fast to implement technological data analysis solutions. With cloud computing, virtually all the resources needed for a Big Data strategy can be used virtually.
The choice of solutions also depends on the objectives and projects to be developed in the Big Data strategy. They can be solutions from Business Intelligence, CRM, Analytics, etc. As you saw, when dealing with the phenomenon of Infoxication, a good strategy is the best choice for companies.
What did you think of the reflection we brought in this article?
