As much as organizations would like perfect data under their
complete governance/management rules and guidelines, those ideal outcomes
disappeared already with the big data era's reality. In the "monster
data" era, it will be much rarer to have complete data management,
especially with the inclusion of more data types, more complexity, and less
reliable data sources coming at organizations at an accelerated speed. This
writing aims to give some advice on deciding which data sources are likely to
get more intense data management. Prioritizing will allow data management to
focus better on balancing its efforts. It will also enable the data usage to be
separated from Ideal/excellent data management allowing new data resources to
enter the organization and grow into data management over time. Prioritizing
data sources for robust data management should consider goals, measures, and change
channels.
Organizations can't focus on all the data coming their way,
and they certainly can't depend on AI supercharged data scientists to find
interesting anomalies from the bottom up. The first cut at the data under management
should directly relate to business/organizational outcomes necessary as a base.
Many organizations set up strategic goals that represent the most desirable for
the circumstances at hand. Savvy organizations plan alternative
strategies/scenarios and practice the most likely emergent strategies. Most
organizations, these days, tie both their functional and operational goals to the
strategy du jour. Savvy organizations look at the end to end processes/journeys
to link goals together to minimize organizational conflict and consider
constituent outcomes as legitimate goals. These efforts would include customer
journeys laced with highly desirable customer experiences. These goals will
help narrow down the data sources that need proper data management, although
much more than in the past.
Prioritize on Real
World Measures
An excellent place to start is where most of the data
activity occurs. Many organizations create heat-maps of highest use and apply
the highest level of governance for these sources regardless of whether they
are operational or decision focused data. There more enlightened organizations
push the data timing to real-time, representing the Database of Now. These same organizations practice data mining to
look at the reality of the current and past situations that may point to future
operation optimizations. The aware organization also mine new data sources like
voice and video for customer experience improvement potential. While much of
the mining is descriptive, the more advanced efforts start prescribing
potential for change and even predicting future behaviors. In this kind of
environment, decision optimization is considered a major important improvement
arena.
Prioritize on Change Channels
Like it or not, things change and almost always get more complicated. Rapid change may mean cobbling together creative responses in a quick fashion. To that end, organizations need to sniff many data sources to change potential impacts that could affect strategy or operations. Sometimes it might be subtle to look at signals and events in these data streams, but it could range to finding emerging patterns within or across data sources. The likely channels would often include competitors, markets, industries, demographics, vendors, suppliers, geographies, legal frameworks, and governmental pushes. Additionally, best practices involve finding new contexts and sources.
Figure 1 Optimal Data
Governance Defined
Net; Net:
Applying excellent data management governance, depicted in
Figure 1, is no longer possible to all data resources. Organizations will have
to focus on the data sources that are key for business outcomes that enable
organizations to survive, thrive, and even optimize their impact. While Data
Management is necessary for specific resources at a high level, the time has
come to identify those particular resources and set data governance levels
across various data sources.
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