A 2015 paper by the World Economic Forum showed that big data might just be a fad. The article certainly raised a lot of controversy, considering the massive emphasis on the value of data technology. However, the article raised some very valid points.
The article was not arguing that big data is going to go obsolete. Rather, the point was that big data is one tool in any organization’s arsenal. It is a very valuable tool, but we need to be careful about overestimating its importance and making unsubstantiated conclusions about its viability.
Companies that take too much of a leap of faith with big data technology could pay the price down the road. They must be realistic about the constraints of big data and be pragmatic with their utilization of it. Companies like Endor have seen the writing on the wall. They recognize that the overemphasis on big data has created problems, so they have presented alternatives.
Data Science Companies Focus on Optimal Data Utilization Rather than Just Emphasizing Data Scalability
Endor is a leading pioneer in data science. Instead of focusing on solely collecting data for their customers, Endor has continually helped users extract more value from datasets.
Companies like Endor understand the risks and develop data science models that account for them. Their data science model provides business predictions at a fraction of the cost of other platforms. Their solutions have been praised by many experts, such as Dr. Alan Boehme, CTO of the Coca Cola Company. Alex “Sandy” Pentland, co-founder of Endor and a professor at MIT has been praised as one of the seven best data scientists by Forbes.
Algeion is another data science company that is making a lot of headway in the industry. They recently announced that they raised $12 million in seed funding to improve the data labeling market.
“The whole point of AI is to mimic human judgement. We combine tech with humans to provide scaled high-quality data,” founder, Nathaniel Gates told Austin Bulletin.
Sisense is a company that is looking for more inventive ways to utilize data for enterprise products. Many of its competitors are looking for consumer applications in big data, but Sisense has found ways to monetize the commercial needs of big data. Forrester recently named them as a leader in BI data analytics solutions.
Companies Need to Understand the importance of choosing the right big data management provider
When you are looking for a big data solution, it is important to choose a platform that accounts for the risks highlighted below.
However, some companies try to handle their data management in-house or use subpar contractors. This can lead to a number of problems.
Risks of using a poorly conceived data strategy
Here are some risks that companies face by overleveraging their big data strategies.
They could make poor decisions in areas where insufficient data is available
Many organizations integrate big data into their decision-making models. This can be a prudent approach when an adequate amount of data is available. Unfortunately, data symmetry is often lacking for certain decision-making models.
Some of my colleagues have discovered this drawback with e-commerce companies they have founded. They use big data to identify promising regions to focus their marketing efforts on. Globalization and the rights of many emerging economies is creating a number of awesome opportunities for e-commerce marketers looking to diversify their offerings abroad.
The problem is that the amount of data available might vary in different countries. Here are some reasons:
- Marketers often aggregate data from popular digital platforms, such as Facebook. Some of these platforms have failed to penetrate the markets of a number of countries. This means that the amount of data on customers in those areas could be sorely lacking. Of course, this does not mean that the customer base is not there.
- Some countries have much stricter data privacy laws. This could hinder an organization’s ability to collect a lot of data on those customers.
- Customers in some areas might be a lot more likely to invest in VPN tools to protect their privacy.
All of these issues could cause problems for marketers trying to expand. They can still use big data to help make these decisions, but they need to keep these limitations in mind.
Data scalability could compromise data quality
My colleague Rehan recently wrote an article for the blog Big Data Made Simple about problems that can arise with excessive data scalability. As he pointed out, focusing too heavily on data scalability can create several problems:
- Organizations could start turning to biased to data sources that will compromise be value of their data
- Organizations might not be able to account for data duplication, which will skew the results of their data
- Organizations might try collecting antiquated data that no longer has any value for their applications
Companies need to be realistic about the risks associated with data scalability. They need to make their investments accordingly.
Compliance risks are becoming much more costly
Companies that utilize big data need to understand the legal complications that they are going to face. One of the biggest changes to reach companies around the world has been the Global Data Protection Requirement (GDPR). This law is one of the most draconian data privacy changes ever passed.
Other data privacy and security laws are expected to get passed in the future. These laws don’t just insist that companies be better about protecting data. They might have strict limits on the types of data that can be collected at all. They need to understand that laws cross jurisdictions. Companies in the United States or Australia will still be expected to comply with the expectations of the GDPR, even though the law was passed in the European Union.
Organizations that are overzealous about collecting data might not recognize these limits. They should be careful about doing their due diligence and being informed if they want to get the most value from their big data strategies.
Companies need to be sensible with their big data strategies
Big data can have a strong effect on companies around the world. They need to make sure that they adopt appropriate measures to make sure that their data strategy is a valuable contributor to their organization. This means doing their due diligence, recognizing the limitations of data and being careful about preserving data quality.