Top Challenges and Solutions for Optimizing Data Warehousing
Many companies have invested in modern cloud solutions for data, but then struggle to realize the benefits they anticipated because of data maturity factors. According to Hayley Ortega, head of client success for Ippon Technologies, “Our clients often come and say, ‘We want to do AI.’ Unfortunately, they often lack the data warehouse or a centralized data solution needed to achieve effective AI capabilities.”
Data integration and quality challenges are common as integrating data into a single, unified view from disparate sources is difficult. These often can be attributed to three primary issues:
- A lack of good data management practices;
- Poorly understood business terms; and
- A hodge-podge of systems built over the years with little anticipation of integrating them in the future.
IT and business leaders need to address these issues by developing data quality checks and data governance practices to drive accuracy and improve data consistency. This enables better data integration processes that leverage data cleansing capabilities to standardize and aggregate data.
Solving for Data Warehousing Woes
To achieve the needed data quality and consistent data governance, a Data Maturity Model (DMM) and assessment framework help identify where the level of ability and execution lies within each discipline of data management.
The overall purpose of the assessment is to establish the enterprise’s baseline knowledge of the current state of its data management practices, allowing appropriate targets to be set and building a roadmap. From this, a cohesive vision of the data management opportunities is created, regardless of what maturity level the initial assessment finds. It provides a tangible artifact to highlight those opportunities and share a single, cohesive message throughout the organization or line of business.
DMMs from Ippon Technologies, for example, offer five levels of maturity:
- Ad hoc – processes are unpredictable, poorly controlled, and reactive, leading to increased risks and inefficiencies
- Isolated – processes exist but are project-based and frequently reactive
- Defined – processes are well-characterized and proactive; organization-wide standards exist and provide guidance
- Managed – processes are measured and controlled; quantitative data is used to implement predictable processes to meet organizational goals
- Optimized – processes are stable but flexible; the focus is on continued improvement and responding to changes in data consumption.
Each level of the maturity framework describes the criteria to be assessed in order to determine which level an organization has reached in its data management maturity. An assessment can evaluate data management at an enterprise level or start with a single line of business. The assessment can also tackle data management overall, from beginning to end, for a single discipline, such as sales, finance, or HR.
Within each of the five levels, there are multiple dimensions and factors to be assessed:
- Data governance
- Data quality
- Data integration
- Data modeling
- Data storage and retrieval
- Data security and privacy
- Metadata management
- Master data management
- Data analytics and reporting
- Data architecture
- Data stewardship