Bridge the distance between data and business
The capability to take advantage of data insights and analytics can make or break an organization. Becoming data-driven produces benefits across customer-facing and internal operations and even the bottom line.
Yet, when discussing data, it’s usage and the results or insights and the impact that data can have on business performance, there’s one word that keeps popping up: Gap
The ROI Gap
Although, with increasing investments in analytical solutions, big data, AI, Neural Networks, Machine Learning, Deep Learning, improving processing capabilities and expanding storage capabilities, either on site or in the cloud, and even on organizational changes, building data focused structures to support data initiatives.
Experience shows that frequently the results fall short on the objectives:
- They fall short on the financial return, with these initiatives exceeding budgets and deadlines.
- They fall short on the insights retrieved from data, due to data quality issues, highly siloed systems, etc., not providing meaningful insights for their decision-making processes.
- They fall short on business objectives, without the expected results on business performance, on customer experience and loyalty, on offer and innovation, on operational efficiency, on reducing inefficiencies and costs.
When this occurs, most organizations will probably accept to live with mediocre, under-performing solutions — expensive failures. Often seen throughout the organization as IT vanity projects.
The Expectation Gap
We see organizations struggling to collect as much data as possible (for the next 5 years the size of data available is expected to grow at a rate of 40% per year), with consistent infrastructure, storage, processing, and analysis investments also increasing.
More and more data is being accumulated across data warehouses, data lakes, always with the perception that more data can be collected, and with it the capability to harness the enormous potential that can be derived from it.
Overlooking the fact that the more data is collected, the more redundant and obsolete data is gathered and the harder it is to analyze it and derive useful insights to feed business decision processes — Leading to a decreasing quality of analysis and insights.
The Trust Gap
A recent poll I’ve been discussing in previous articles, brought forward some disturbing conclusions on the way C-Level executives consider data made available for their decision processes, that might in some degree be a result of the problems I mentioned before.
Truth is, these findings show exceptionally low levels of trust, both on data and on the insights derived from data, with report of decisions being made ignoring the available data or based on gut feelings.
We can’t avoid but to thinking that serious structural problems exist on the usage and handling of data for corporate decision processes, and the outcomes, can’t be much better than common guess work, however educated it might be.
Bridging the distance
Data strategy is business strategy. The ultimate purpose of an organizations data is to create business value, so any data strategy must be oriented towards the organization’s strategic priorities and key business objectives and any data related initiative must be entirely supported on business strategy and objectives.
IT must step back and allow business stakeholders/units to drive these initiatives. These are the people who know what the business problems are, is needs and objectives.
Giving the control to the business, building the business case with those willing to defend it, those who can easily identify business pain points, while solve some of challenges usually associated with these processes, as lack of cross organization involvement or resistance to change.
Having business stakeholders that can passionately and effectively articulate the impacts and benefits of a data initiative and that will be eager to defend the project — Transforms a traditional resistance point into an evangelist, with enormous impact on the trust of the insights being produced and the capability to quickly move from insights to actions.
Failing to support any data initiative on strong business cases, anchored on clear business objectives, transforming data initiatives into technological initiatives will impact the success of these initiatives, often seen as just another siloed IT project with no perceived value from the business side.
The role of IT in this process is to find the right technology and support the business units in this journey.
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