Analysis of enterprise data is a dynamic endeavor. As new tools and applications are found to handle business use cases, new data arises that in many cases produces new opportunities, but these may require new approaches to analysis and selection of new tools. This report seeks to provide some planning assumptions in this dynamic world in a way that can serve your decision-making today.

The History of Big Data

Analysis of data was the first, most fundamental use case in computer science. But with the rise of the need for Internet size data analytics things had to change. Pioneers at Google created breakthrough approaches that enabled them to scale their capability to analyze data in new ways. In 2003 they published a paper titled the Google FIle System, and soon thereafter a paper on MapReduce and BigTable. By publishing their approaches in an open way and strongly supporting open source development of tools like Hadoop, they kicked off a wave of approaches known in the community as Big Data

The Status of Big Data Today

The growth of Big Data has been foundational to the ability of enterprises to leverage Artificial Intelligence. The two megatrends have to be tracked separately, but are related (you can analyze Big Data without AI, but it is hard to do AI without Big Data).

• Some estimates indicate there may be as many as 1 Trillion sensors in the globe already. Sensors generate data, which needs analysis!
• Breakthrough methodologies and new scalable data approaches based around Hadoop hold great promise in sensemaking over large data sets.
• Sensemaking is key to success in most all other technology advancements.
• Ethical considerations around privacy and security over data will only grow.
• How ethical considerations are resolved will depend on culture, politics, other factors.

Open questions decision-makers should track include:

• Can there be widely accepted ethical frameworks for data and its use?
• Who will individual citizens trust to analyze their personal, home, auto data?
• Can behavioral analytics enhance service and security?
• What can adversaries do to attack enterprises via attacking data?
• How can hybrid cloud architectures be leveraged for advanced big data analytics

Cybersecurity and Big Data

Threat dynamics around new big data analytics capabilities include the juicy new targets that large stores of data are proving to adversaries. The news is full of examples of big data bases being found open on the Internet. Additionally, new data tools are being used by both defenders and adversaries to seek ways to get a leg up in cyber conflict. Defenders use big data to look for early warning of attack. Attackers use big data to look for new avenues of approach and new vulnerabilities.

Impact of Big Data trends on Due Diligence assessments:

The trend of Big Data is an increasingly important element of corporate Due Diligence.

• On the sell side: Firms should ensure their data architectures are optimized and data is protected. Privacy considerations are also key and should be optimized prior to sell, as should compliance issues (especially compliance with GDPR and California 2020).
• On the buy side: Buyers should pay particular attention to the security and ethics around data policies and architectures.

Strategically, the evaluations of firms is an art requiring assessment of how unique the capability is and how much in demand it will be in the market. We provide due diligence consulting via our consulting arm, OODA LLC.

Additional insights to inform your business strategy in an age of digital transformation can be found in our OODA Members Resources Page.