If you’re working in a more technology-driven industry, then chances are you’ve heard the term “data intelligence” before. But what is data intelligence? And more importantly, how can you use it to your competitive advantage? This guide explores data intelligence and how its best practices can benefit a variety of industries.
- What is data intelligence?
- Types of data intelligence
- Who uses data intelligence?
- Pros of data intelligence
- Best practices for using data intelligence
- Data intelligence for a growing data market
What is data intelligence?
Data is only suitable for decision-making if it is trustworthy, accurate and timely. This is where data intelligence comes in. In the most basic sense, data intelligence is a way or system of using data to make better decisions. You can optimize your data intelligence either by analyzing existing data or collecting new data.
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Data intelligence now mostly relies on artificial intelligence and machine learning techniques in order to make predictions or recommendations based on collected data. According to The state of AI in 2021 global survey by McKinsey, at least 5% of operating income is now attributable to the use of AI. Use cases include making better products and service development, marketing, sales, strategy and corporate finance decisions. Some companies attribute as much as 20% of their operating income to AI.
Types of data intelligence
Data intelligence is driven by metadata or the data that tells you about the data. These are the primary metadata categories:
- Behavioral: This type of metadata is used to understand how users interact with data, applications and services.
- Technical: This category of metadata is used to understand the technical aspects of data, such as schema and table definitions.
- Business: Business metadata defines data handling policies.
- Provenance: This type of metadata tells the story of where the data came from (lineage), how it was collected and how it has been transformed over time (versions).
There is also active metadata, which is supported by AI and ML and augmented by human intelligence. Active metadata gathers internal insights about how people use the data.
Who uses data intelligence?
Data intelligence is used by businesses of all sizes in various industries. Retailers use data intelligence to predict consumer trends and make decisions about which products to stock on their shelves. Banks use data intelligence to identify fraud and prevent money laundering, and insurance companies use data intelligence to assess risk and set premiums.
Other use cases include:
- Healthcare: Track the spread of diseases, predict epidemics and personalize treatments.
- Transportation: Optimize traffic flow, reduce congestion and improve safety.
- Manufacturing: Predict equipment failures, optimize production processes and reduce waste.
- Supply chain management: Optimize inventory levels, predict demand and route shipments.
- Law enforcement: Track crime, predict terrorist attacks, and track the movement of people and goods across borders.
- Human resource management: Identify talent, predict attrition and improve employee retention.
- E-commerce: Personalize recommendations, optimize pricing and prevent fraud.
Pros of data intelligence
Contextualizes data sets
When dealing with an enormous amount of data, a lack of proper context can end in disaster. Conversely, when data sets are properly contextualized, this data intelligence ensures that data sets are easier to understand and draw conclusions from.
Improves data quality
Data science students are often told that the cardinal rule of data is that it can only be helpful if you trust its quality. Bad data is unclear, inaccurate, stale, unreliable and untraceable. All of these qualities make it difficult to work with, which is why data intelligence is so important. By using data intelligence, businesses can monitor data quality and take steps to improve it.
Improves data accessibility
Data that is scattered across different organizational silos is difficult to access and use. Data intelligence can help businesses break down these silos and make data more accessible.
Simplifies lineage tracking and audit performance
As data moves through an organization, it often undergoes a number of transformations. This can make it difficult to track where the data came from and how it has changed. Data intelligence helps businesses keep track of data lineage and perform audits, which helps to ensure data accuracy.
Best practices for using data intelligence in your business
If you’re thinking about using data intelligence in your business, there are a few best practices that you should keep in mind.
First, make sure that you have a clear understanding of your goals and objectives. What do you hope to achieve by using data intelligence?
Second, invest in quality data collection and storage tools so you can be sure your data is accurate and reliable. In addition, invest in the latest data intelligence tools and platforms that leverage AI and ML to help you make better decisions. It also helps to hire a team of skilled in-house data scientists or onboard a managed service provider who can help you make the most of your data.
Finally, make sure you have a plan for how you will use the insights generated by your data intelligence efforts to make the best possible decisions for your business.
Data intelligence for a growing big data market
Enterprises are spewing out an astronomical amount of data from various sources, ranging from day-to-day business transactions to data collected from big data sensors that store weather information. Recent estimates indicate that the world produces over five exabytes of data — that’s five followed by 18 zeros — per day, with a large portion of this data being unstructured. Conservative projections suggest this figure could exceed 463 exabytes by 2025.
Businesses have known for a while now that they can leverage data for competitive advantage. That’s why every corporation wants to be data-driven and reap the benefits of doing so. Data intelligence ensures that data is accurate, accessible, and applicable to real business problems.