As organizations strive to continue responding to market changes and opportunities, there is an increasing need for operational and strategic information to better support the business. Before committing to or starting work on a large Business Intelligence (BI) initiative, which is typically a significant investment, organizations are increasingly recognizing the advantages of conducting a program assessment.
Look Before You Leap into the Data Lake
If the concept of a data lake is confusing to you, don't worry because you're not alone. A primary reason for this confusion is that the definition of a data lake seems to change depending on which constituency you ask. The big data community will define it as a central location for all your disparate data sources stored in its native format in Hadoop. Even within the big data community, it may be called something different, like enterprise data hub, depending on the vendor you're speaking with. In the Business Intelligence community, a data lake is defined as a staging area, or landing area, for your source system data. They make less of a distinction about where the data is stored.
The two questions I'm asked most often include:
1. If I build a data lake, does it need to be in Hadoop?
2. Is there any value in building a data lake?
It amazes me -- but probably shouldn't -- how many organizations accept the data they depend on every day at face value, and yet do not understand how it comes together. They seek to be "data-driven" yet they are not willing to invest in understanding how data drives the organization, how it could be dramatically improved, and then they wonder why they are missing that performance edge that smart executives seek.
Enterprise performance depends on having certainty about the derivation of the entire data chain. If you drive a car, you don't worry about how the car works, but you do develop some degree of trust in the quality of engineering that put the car together. Auto engineering has over time much smarter about the entire process, from design through delivery, and engineers have built quailty in to their products in part through ensuring all components work well together.
Are businesses undervaluing the impact of Master Data Management (MDM) initiatives? If they are not looking at the many, often unexpected, ways that data management can impact the top and bottom line, they might very well be doing so.
It is firsthand knowledge that data management initiatives are seldom viewed as an opportunity to create significant value within an organization. You may be wondering how to entice your stakeholders with an MDM project when the ROI may seem intangible. Although it may seem elusive, the business case below outlines how one company realized a return of over $75M on a $25M investment.
Second in a series of articles that describes foundational steps to enable agile data warehouse development. The first installment described how to develop a business conceptual model as a starting point. This article shows how to build a "grass roots" data governance capability that features broad business stakeholder involvement.
With the focus that companies have on everything “data” – analytics, reporting, dashboards, Big Data, predictive analytics, decision science and so on – data governance is also increasing in importance. A good data governance program gets business stakeholders involved in deciding on data definitions, and in supporting or sponsoring consistent data usage across an organization.
A look at best practices learned while using agile development methodologies for enterprise data management solutions, including an enterprise data warehouse, data governance, data quality, integrated analytics and reporting. It starts with building a strong foundation, and then delving into additional innovative solutions leveraging what gets learned and continuously improving and expanding on it.
A successful agile data warehouse can speed the delivery of information to organizations. Ideally, initial raw data can be delivered to analysts quickly, then integrated into a formal enterprise data warehouse (EDW) for more robust and broad reporting in ensuing development cycles.
Why you need a modernized data warehouse and BI environment and what it looks like
Most credit unions grew up in an era where custom-built data environments reigned supreme. Excel, SQL, and Microsoft Access were the workhorses of the data warehouse. Everything from member data to transaction data were tracked across multiple applications and spreadsheets. What started out as a solution for data to a handful of users was expanded and layered upon overtime, becoming unwieldy and inefficient
Effective visualizations support good data storytelling. They guide the audience through the data in a way that allows them to quickly understand and easily draw conclusions. Understanding the audience, taking the time to prepare the data, and choosing the most appropriate visualization type, ensures visualizations will deliver clear information that can then be acted upon. The sections below outline the essentials to create both compelling and actionable visualizations for any audience.