Introduction
In the past decade, we've seen a seismic shift in how companies manage their data. This shift has led to the creation of what we call the intelligent enterprise. The intelligent enterprise is a business model that requires an organization to have a strong data foundation and the ability to leverage vast amounts of data in order to make better decisions at every level of their company. In this blog post, we'll outline five key components of an intelligent enterprise:
They have an established data management practice
Data management is the practice of managing, organizing, and protecting company data. Data can be anything from customer information to data related to the manufacturing process or even financial records.
Data management involves:
- Creating a secure environment for your organization's sensitive data
- Ensuring that all employees understand what types of information are considered confidential and how it should be handled
- Creating policies about who has access to different levels of information within your company
They have implemented data ops to ensure quality of data
Data quality is the ability of data to be trusted, accurate and complete. It's important because it can help you make better decisions. A good data quality strategy will ensure that your organization has the right level of focus on improving their overall data quality over time.
A good example is an insurance company that needs access to accurate customer information in order to provide them with the right products at the right price point. With poor quality control measures in place, this company could end up offering a product that wasn't appropriate for its customers' needs – and losing out on potential sales as a result!
They manage their data stewardship
Data stewardship is the process of managing data from creation to retirement. It ensures that data is used for its intended purpose and in a way that does not violate privacy rights or other laws. Data stewardship involves managing the lifecycle of data, including storage and disposal:
- Ensuring compliance with privacy regulations
- Controlling who has access to information
- Ensuring security measures are implemented at all times
- Training employees on how best to use information appropriately
- Monitoring usage statistics so you can make adjustments if necessary based on trends
- Having clear policies about what types of information can be shared internally or externally
- Using technology like encryption software when transmitting sensitive materials across networks
They have data scientists spend more time analyzing than harvesting data
Rather than doing the traditional "data collection" tasks that most people think of when they hear the term "data scientist”, the main task of a data scientist should be to find the signal in a sea of noise. The signal is what you're looking for, but it's buried under a lot of other stuff – the noise. The goal is to extract that signal from all the noise surrounding it, then use that information to make predictions about future events or come up with new ideas for products or services.
Data is a critical component of any business, but it's not enough just to have access to lots and lots of information: you need people who can make sense of the data you have and turn it into something useful for your business.
Data is consistent, reliable, and trustworthy throughout the organization
Data is delivered at the speed of business.
Data is delivered in a way that is secure and compliant with your company's standards for data governance and privacy protection.
Data can be easily consumed by any user with no special training or tools required (such as SQL).
It's important to note here that "consistent" doesn't mean identical. It means that no matter where you are in the company or who you're talking to about data – whether it's an analyst or a manager – they'll get the same answer when they ask for something like: "How many customers do we have?" The same goes for reliability; if someone asks for some information and gets it back as an Excel spreadsheet, then they can rely on it being in the same format if they request it again later down the road. And finally trustworthiness; if those working with the data don’t even trust it, then surely those receiving the data cannot. Consistency and reliability give way to trust. You can’t have any without the other.
Conclusion
Data is a strategic asset for the intelligent enterprise and it's essential that you know how to manage it. Data stewardship must be taken seriously by all departments so that everyone understands how their actions can impact the quality of information being delivered throughout the organization.
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