Big Data in Business: Data Requirements for AI-Driven Businesses

What’s this about: The driving factor behind the success of many today’s organizations is the implementation of artificial intelligence (AI) and data solutions, which is why so many are pouring resources into the area. Whether the company’s activities involve AI, data science, advanced analytics, machine learning, or some other related process, the goal is to use data to increase revenues and efficiency.

What is Big Data?

For a company to digitally transform or turn itself into an AI-driven business, there are many data and organizational requirements that have disrupted business models across industries. These companies must make a complete data transformation if they want to leverage the power of AI. The combination of modern AI algorithms, cheaper computation and massive amounts of data has led to this explosion of AI in business, and harnessing data is the key to success. Adopting AI is more than just integrating a new type of technology into an organization’s tech stack. It is a full paradigm shift affecting every aspect of the business.

When we talk about data requirements for an AI-driven business, we are often talking about “big data,” which is the term applied to massive and diverse sets of continuously expanding information. Humans produce data each day, meaning big data never stops growing and becoming available to organizations. The data sets that make up big data often come from various data sources and are too complex for traditional data processing software.

Gartner defines big data as:

“high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

Artificial intelligence and big data go hand in hand. They have become synergistic. AI is useless without data, and at the same time, it’s difficult to extract intelligent insights from data without AI. Hidden patterns that are hard to expose by non-cognitive software processes can be pinpointed with AI solutions. AI systems rely on the gathered data to analyze inputs and improve their patterning processes, which results in an increasingly accurate diagnostic. The more quality data that is fed to the AI system, the better the AI system is at making accurate decisions, providing recommendations, and improving on models.

Key Characteristics of Big Data

The data required for an AI-driven business must possess a few key characteristics. There’s no need to be intimidated by the complex definitions around concepts like big data! It can be remembered as the three V’s:

Variety: The datasets must contain high volumes of low-density, unstructured data. While data used to come from one place and was delivered in one format, such as excel or csv, it is now available in many non-traditional forms like video, text, pdf, graphics, and more. It can come from a variety of sources, such as social media or wearable devices.
Velocity: The data must also have a high velocity, with the term “velocity” referring to how fast it is coming in. Some of this data will come in real-time, while some of it will come in batches.
Volume: The data must come in unprecedented volumes. It’s estimated that around 2.5 quintillion bytes of data are created each day in this new data-driven world, meaning companies have Terabytes, or even Petabytes of data in storage. This volume of data is key to establishing an AI strategy.

Selecting and Preparing Data for AI Solutions

Once an organization has decided to transform into an AI-driven business, it must collect all of the data, select the “right’’ data, and put it through a “cleaning process” to ensure it’s ready to be fed into an AI system and leveraged for insights. These are key steps required before any AI and machine learning models can be implemented into the business, as it avoids a “garbage in, garbage out” scenario, which is bad for machines and humans alike!

Before cleaning the data, the company must first select the data needed for the prioritized business opportunities and use cases. While this seems obvious, many organizations suffer from a disconnect between data engineering teams and the business functions, resulting in the integration of data sources that are not needed.

To begin gathering and selecting the right data for AI solutions, companies should ask themselves a few questions that make up the process of Data Due Diligence:

What data exists internally?
Do we need external data?
Where does the data reside?
How can the data be accessed?
Is it high-quality data?
Are our datasets compatible?
Can it be linked with other data?
How can the data be applied to the company’s use cases?

When it comes to internal data, they are usually spread out in multiple data silos across legacy systems. They might even be possessed by different business groups with different priorities (and different formats). To overcome this, the business must consolidate and integrate data silos and fragmented systems to create an accurate view of the entire organization. This process will help ensure the data is accurate and rich, ready to be implemented into AI models.

Once data has been selected, it’s time to clean them. But what exactly does it mean to clean data? When we say data needs to be cleaned, that’s not to suggest there is something “wrong” with the data itself. (Let’s go easy on them!) Instead, it’s the formatting that needs to be consistent.

Data cleaning is an intense process with the goal of achieving high-quality data, and it is one of the most important steps for creating a data-driven culture. It involves a few key steps, including:

Correcting spelling and syntax errors
Fixing mistakes like empty fields
Standardizing data sets
Identifying duplicate data points

These processes are crucial if an organization wants to truly harness the power of big data. Data scientists actually spend 45% of their time preparing data, rather than on actual machine learning tasks.

Data Storage Requirements

With all of the data cleaned, they’re ready to be used for machine learning. But there is another key step: data storage. After data is in a central warehouse, they can be queried and analyzed for insights.

There are many different options for storing data, with one of the most popular being cloud-based storage. But before a business can settle on a storage solution, it must consider the very specific storage requirements for machine learning and AI workloads, such as:

Scalability: To increase the accuracy of machine learning and AI models, businesses must collect and store more data each day.
Accessibility: Data must be continuously accessible since machine learning and AI training requires entire data sets to be read and re-read.
Latency: Another key requirement is latency of I/O, or input and output. By reducing the I/O latency, machine learning and AI training can also be reduced by days or months.
Parallel Access: Machine learning and AI training models split activity into multiple parallel tasks meaning the algorithms access the same files from multiple processes or multiple physical servers at the same time.

One of the most effective data storage solutions an AI-driven business can embrace is specialized and dedicated cloud storage. AI data is often unstructured, and 80 to 90 percent of total data generated and collected by organizations is unstructured. Unstructured data is not arranged according to a pre-set data model or schema, meaning they cannot be stored in traditional databases.

Unstructured data is highly valuable for AI-driven businesses, containing a wealth of information that can help guide business decisions. But unstructured data has traditionally proven difficult to analyze, until AI and machine learning tools have turned this around.

Unstructured data can be generated by both humans and machines.

Here are some of examples of human-generated unstructured data:

Text Files: Word processing documents, spreadsheets, email, presentations, log files, etc.
Social Media: Data extracted from social media platforms like Twitter, Facebook, Instagram, LinkedIn, and Youtube.
Mobile/Communications Data: Text messages, phone recordings, chat, messaging, and collaboration software.
Media: Digital photos, video files, and audio.

Here are some examples of machine-generated unstructured data:

Sensor Data: Traffic, weather, and environmental sensors.
Digital Surveillance: Digital surveillance data in the form of photos and videos.
Satellite Imagery: Weather data and landforms.
Scientific Data: Oil and gas surveys, seismic imagery, and atmospheric data.

To leverage all of these unstructured data for AI and machine learning, the most successful AI-driven organizations often opt for a cloud-based storage solution like a data lake. A data lake is a centralized repository where organizations can store all of their structured, semi-structured, and unstructured data in their current form. This enables various types of AI analytics, such as dashboards, visualizations, real-time analytics, and machine learning. Different from traditional databases, which are “schema-on-write” (meaning the structure of the data is created prior to filling the database), data lakes are “schema-on-read,” which means the structure of the data can be left until the moment the data is accessed.

Storage solutions like data lakes are becoming important for the success of AI-driven businesses. They enable companies to increase operational efficiency, make data available from departmental silos, lower transaction costs, and offload capacity from data warehouses.

Implementing an AI Strategy With Data

Once an organization goes through these various stages of data transformation, they can finally begin creating and implementing an AI-driven strategy. The key is to move from experienced-based, leader-driven decision making to data-driven decision making.

One of the most valuable aspects of becoming an AI-driven business is that an effective AI strategy means employees at all levels will be able to augment their judgment with data-driven recommendations and predictions, allowing them to arrive at far better business decisions than possible with humans or machines alone.

It’s also important to recognize that this transition will not happen overnight. (Good things take time!) Business leaders must prepare their employees and businesses to make the change, starting with those at the top. By adopting a test-and-learn mentality, mistakes become discoveries. This learning process allows for the next version of the strategy to be more effective, and development will speed up quickly.

One thing is certain: any effective AI-driven transformation must start with data. By understanding data, selecting and cleaning them, and eventually finding the right storage solution, business leaders and employees will be able to dramatically improve their decision-making processes like never before.

>>> Make sure to look out for the next installment of this series, where I’ll explore how data will affect revenue streams and valuations for AI-driven businesses.

Giancarlo MoriJune 23, 2022