Digital transformation is real, and it is upon us.
It’s a matter of “disrupt or be disrupted.”
Organizations are driving transformative initiatives to improve financial performance and competitive position in their industries.
Examples of these initiatives include deepening customer relationships, optimizing operations, personalizing healthcare, and preventing fraud.
The key factor driving the success of these initiatives is the ability to fuel them with trusted and timely data.
It’s pretty simple: Successful digital strategies are built on data.
The competence you build around data management will determine how successful your digital strategy is.
Or, put another way, your digital strategy will be only as effective as the data that informs it.
Odds are, however, that managing data “the way you always have” won’t cut it. IT leaders are looking for ways to boost data management productivity to make better data available to all, faster.
Data Management Trends
It’s time to think differently about data and data architecture. For decades, the focus has been on business systems and processes. While those are still important, the ability to deliver better, more timely, more complete data to your business initiatives will be what truly differentiates your organization in the marketplace. But, most IT budgets are growing slowly, so you also need to factor in doing more with your current resources.
The challenge of managing enterprise data has never been greater. To unleash the power of data, your IT organization must be able to manage:
1. More Data:
- Data volume: 15.3 zettabytes per year in global data center traffic.
- Data complexity and variety: There are many new sources and types of data, from within and outside the enterprise.
- Data velocity: The rise of Internet of Things (IoT), with 20 billion connected devices, means always-on data streaming.
2. More users: With 325 million business data users and growing, everyone from business analysts to citizen data scientists to data stewards wants direct and timely access to data.
3. More integration patterns:
- Movement to cloud: ERP suites are breaking up and moving to the cloud.
- Analytics technology: The industry is moving to new technologies such as big data, NoSQL, and predictive analytics to complement data warehousing.
- Experimentation: Users now want to use data to quickly form a hypothesis, try it out, either succeed or fail, and iterate quickly. It’s all about speed over precision until they prove that a hypothesis is of value.
Download this paper and learn how the machine learning-based innovations in CLAIRE are driving a big leap in data productivity.