To make informed decisions in complex matters may require access to multiple sources of stored information (data), throughout the business enterprise. However, security, privacy, and regulatory concerns may drive IT departments to restrict access to these sources. We know from our study that functional silos can be detrimental to the performance of a business, but how do data silos influence decision outcomes and inherently the overall business performance?
“Information is the oil of the 21st century, and analytics is the combustion engine.” ~ Peter Sondergaard, Senior Vice President and Global Head of Research at Gartner, Inc.
#1 – DAMA-DMBOK: “Data Management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.”
A shortlist of data management practices, and concepts are:
- Data Governance and Data Stewardship
- Data Architecture
- Data Modeling & Design
- Data Storage
- Data Security
- Data Integration & Interoperability
- Documents & Content
- Reference & Master Data
- Data Warehousing & Business Intelligence
- Meta-data Management
- Data Quality
#2 – SAP: “Data management is the practice of collecting, organizing, and accessing data to support productivity, efficiency, and decision-making.”
According to SAP, the data management process includes a wide range of tasks and procedures, such as:
- Collecting, processing, validating, and storing data;
- Integrating different types of data from disparate sources, including structured and unstructured data;
- Ensuring high data availability and disaster recovery;
- Governing how data is used and accessed by people and apps;
- Protecting and securing data and ensuring data privacy.
Data management is the spine that connects all segments of the information lifecycle. Data Management works symbiotically with process management, ensuring that the actions teams take are informed by the cleanest, most current data available — which, today, means tracking changes and trends in real-time.
To make the right decisions, people need the right information. For instance, for banks to entrust their account managers to provide clients with a suitable line of credit or business loan, they need access to actual and factual information about their customer’s personal credit score, business credit score, financials, time in business, annual revenue, and industry.
Suppose the required information resides in different systems and databases. Banks need a reliable business process to gather that information. Because from a security or privacy standpoint, providing account managers with overall access to enterprise data sources is not an option, let alone the capabilities to extract the information from these sources ─ this will need to be automated.
One solution is to run a series of standard business reports on a set interval and allow the bank’s account managers to access these reports on a per-customer basis. Another solution is to allow account managers to run these data aggregation processes on-demand, with or without customization capabilities. In both cases, individual access to these data sources isn’t required. For some of the business functions, report-driven decision-making may very well suffice.
Regardless of how we design these business processes, one of the challenges of storing information is the quality of the data. Is the data actual? Is it factual? When was it stored? When was the data last checked? Is the data secure? Is the data mostly structured or unstructured? How can the data be unlocked?
If the quality of the information needs to be of the highest standard, given our example of a bank, because of the level of exposure to credit risk, data management practices become more prevailing, often leading to the creation of a data warehouse while enforcing strict data governance and customer onboarding practices to comply with international banking rules & regulation (BCBS 239 / FRTB / IMA / KYC).
In any high-level risk decision, report-driven information based on unqualified data and standardized reports may not provide the required information. Account managers need to include real-time information, for instance, from stock exchanges, as well as unstructured information, from news sources or lengthy sector outlooks, and cross-reference that information with historical (customer) data to make better informed, data-driven decisions.
Or, if we are unsure what to look, a trend or recent change, we will need to resort to big data (data lakes), data analytics, algorithms, and machine learning.
ROUNDMAP™ and Data Silos
While bureaucracy often leads to functional silos, a known side-effect of the division of labor and specialization, business applications tend to get caught up in functional silos, thereby creating data silos: salespeople store customer-related information in a CRM system, production workers keep records in their ERP system, and Human Resources rely on HRM systems. Ergo, functional silos and data silos often go hand-in-hand, thereby reinforcing the detrimental effects of siloization.
To cross the functional silos, ROUNDMAP offers a series of practical solutions. However, to cross the data silos, firms need to resort to data management practices, creating data warehouses or customer data platforms, to support integrated business management practices.