Managing the volume, velocity and variety of data in today’s enterprise requires large teams, diverse skill sets and complex workflows.
Companies often manage hundreds of applications and thousands of databases and information inventories are growing much faster than IT budgets. Automation, efficient collaboration and knowledge-sharing are critical to success.
The current enterprise landscape spans a myriad of technologies, packaged and custom applications and on- and off-premise deployment. With hundreds of applications and thousands of databases, it can be very difficult to see the big picture and find the relevant data.
Enterprises require the ability to manage large, diverse information inventories in order to maximise reuse, minimise redundancy and make the best use of their information assets.
Of course, the ever-increasing volume of data is one of the widely reported trends in enterprise data that affect virtually every organisation today. It’s not just the growth in volume, but also the increase in the pace of change and the variety of the data coming out and contributing to the complexity of the data landscape. There is tremendous pressure on IT organisations to manage and control this data.
More concerning is that the percentage of data that is effectively utilised seems to be relatively small. This may cause data to be sidelined into silos so it cannot be discovered across the organisation, affecting the ability to make good decisions.
Some organisations struggle with quantifying the value of their data and justifying the work necessary to manage it properly. Enterprise data is no longer strictly relational in nature. It has to be integrated, rationalised and normalised for the business to realise its value.
There are tremendous competitive advantages available to those who can use their data properly, but it requires that the data be carefully designed, integrated and accessible to the business teams who can create the business advantage.
According to studies by analysts, information is now being considered as ‘one of an organisation’s most critical and strategic corporate assets.’ However, the consequences of poor or inadequate data modelling are still not widely recognised, often because those consequences might not reveal themselves immediately.
In a recent study1 across 530 senior executives from North America, Asia Pacific, Western Europe and Latin America in a broad range of industries, a clear link was revealed between financial performance and use of data. A strong positive correlation demonstrates that data-driven organisations outperform those that do not have a handle on their data.
The study found that only 11 per cent of respondents felt their organisations made substantially better use of data than their peers, even though more than one-third of that group was comprised of top-performing companies. In contrast, of the 17 per cent of executives who said their companies lagged behind their peers in financial performance, none felt their organisations made better use of data than their peers.
Marketers now have access to unprecedented insights about their customers. Much of this is driven by automated capture of behavioral information - if you can see and understand what customers are doing, and how they are behaving in relation to your products and services, you can use that insight to tune your offers to the marketplace. A lot of this is now happening through mobile devices.
There are three widely reported trends that impact every organisation today:
- A massive increase in enterprise data coming from more sources, and being refreshed faster. Both structured and unstructured data are growing exponentially.
- A reduction in the actual volume of enterprise data being utilised by organisations (approximately 20 per cent in 2001, approximately five per cent today).
- With more data comes increased opportunity, but also much increased risk of data miscomprehension and non-compliance with mandatory regulations.
These trends create serious and perennial problems - problems that are growing over time and not going away.
So why can’t organisations make more effective use of information? In short, it’s information obscurity: enterprise data isn’t just big, it’s complex. Enterprises have hundreds of systems and hundreds of thousands of data elements.
If data is in a hundred different systems, if it’s escaped the data centre on mobile devices or migrated to the cloud, where do you go to find the right data, and how do you interpret it? It’s no surprise that most organisations can’t leverage all of their data - in many cases, users can’t even find it.
In many companies, IT departments are being challenged to do more with less - less funding, less headcount, and less flexibility. As data volumes continue to explode, and the demand for instant, informed decision-making is a basic expectation, there is considerable exposure for those that need to make software selections, or to utilise that software in an effort to meet basic service levels.
There are continually more apps running on more devices connected with more databases. In order to work more efficiently and effectively, database professionals must use the right tools to manage their data. Cost and agility are recurring obstacles. The challenge is to make the data truly usable. How does the data become valuable to the enterprise?
A professional data modelling tool has interactive capabilities that allow for the development of sophisticated, complex data models that can be fully utilised in a production environment. Tasks such as code generation and reporting functions are considered to be standard.
By adopting a data modelling tool, an organisation can not only enjoy the benefits of fast and user-friendly data model design and development; it can also more readily share, re-use and re-purpose data models, allowing it to improve data quality and compliance through standardisation and collaboration.
When done well, an enterprise data architecture is an organised model of a business’s data and information assets, which contains a complete representation of the contents along with metadata that describes it. Data modelling is used to get a consistent, unified view of all of a company’s data and answer the following questions:
- What are the data elements?
- How are they defined?
- Where are they stored?
An enterprise data architecture creates value by managing data redundancy, enabling organisations to integrate and rationalise their data, and increasing data quality by defining the relationships and constraints around the data to ensure that it’s correct at the time of capture. The enterprise data architecture helps to make everyone in the organisation aware of what data is available and allows them to better understand that data. Data models make your data more accessible.
Having a solid foundation with an enterprise data architecture that includes well-defined models can establish the structure and process that is needed to keep up with the volumes of data that are coming in. Data modelling is more important than ever before, and organisations that seek to fully leverage the value of their data assets must make data modeling a priority.
Once your data architecture is established, the next step to take with your data is to share it across your organisation. Collaboration and syndication of the data across the organisation can give your business analysts and decision-makers the visibility they need. Modelling is a critical part of the process to unlock the value of data.
Collaboration creates richer, more usable metadata. Social engagement and crowdsourcing collects corporate knowledge and best practices, while cross-functional collaboration captures definitions, taxonomy, policies and deployment information.
Syndication makes the data available where and as needed. It allows you to better manage your valuable data assets and improves the usability of data and metadata across the organisation. Once you can find, know and protect your data, you’ll be in a better position to automate and document your deployments.
The benefits are numerous and include establishing standards that ensure consistency and improve quality for the data. Even with the cost of investing in a new product, you can demonstrate a return on investment and operational cost savings in a short time period.
Spreadsheets and workflow diagram tools cannot scale efficiently to meet rapidly evolving business needs. Having a proper data modelling and management strategy will result in better use and reuse of your important data assets, and therefore in better decision-making.