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Data Isn’t Everything: Challenges of Data-Driven Business

The current wave of excitement about data-driven business and data-related technologies might lead one to think that data is panache for poor organizational performance. Despite all the attention on data including millions of dollars spent on data management, business intelligence, and analytics projects, many organizations still struggle to gain value from the investment. According to a 2014 survey by The Economist Group, 73% of respondents said they trust their intuition over data when it comes to decision-making. While data definitely has the potential to improve organizational performance, it has some limitations too. Hence, it would be prudent to know some of the limitations of data, or rather situations where even quality data doesn’t add much value to the enterprise.

Data is normally obscured and biased

Most data that is analyzed in enterprises is structured data that is stored in databases. The data that is stored in these databases is transformed from the unstructured natural format into a structured format after the raw data is gathered, curated, and finally stored. This structured format is driven either by the application (including the database) or by an individual’s predispositions and experience. For example in activity-based costing (ABC) analysis, if the application (and the database) can only capture the start and end time, but not the actual effort of the activity, then reporting and analytics on the activity effort would never be possible. So the data context is either predetermined or distorted. This means “raw data” that is captured, curated, and stored is not only obscure, but also biased.

Data doesn’t always translate into actions and results

Even if the data quality is good and unbiased, translating data into insights, strategies, and actions depends on the organization structure, proper training, and empowerment of staff to take actions, among other aspects. This image is a real example from one of the main streets in Calgary, AB, Canada, where gasoline price per liter at Shell (at 87.9c) is about 6 cents less than the gasoline price in Esso (at 93.9c). While the competitor’s data for the attendant in the Esso gas station is right in front of him, he is not able to change the gasoline prices due to the approval he needs from his manager. Nothing is more frustrating than having the most timely, accurate data and insights but still not being able to take any action. No value is created by data and insights if they are not acted upon. If the insights are not put into action, then analytics is not providing any value. Thomas Edison is believed to have said, “The value of an idea lies in the using of it.” Ernest Hemingway said, “Never confuse movement with action.”

Relevancy of data is a function of time and space

Quality data today may not be relevant at a future time in a different space or jurisdiction primarily due to changing business needs and government regulations. For example, say within an enterprise that shipment in plant A could be based on delivery priority while shipment in plant B could be based on customer type. So for plant B, delivery priority data is irrelevant or unnecessary. However, most often the relevancy of data is misunderstood, and many organizations spend a lot of time and effort in managing data that is unnecessary. This is a perennial problem in information management, and researchers Martha Feldman and James March reported way back in 1981 that managers often ask for data and information that they don’t use.

Data has the potential to cause analysis-paralysis

Presently we have capabilities to generate, capture, and process huge amounts of data. According to Eric Schmidt, Chairman of Google, every two days we create as much data as we did from the dawn of civilization up until 2003. According to IBM, every day, we create 2.5 quintillion bytes of data (1 quintillion = 1 followed by 18 zeros). This situation will result in more challenges in getting quality data and ultimately deriving meaningful information. According to William McKnight, author of Information Management: Strategies for Gaining a Competitive Advantage with Data, “It’s not just spitting out information for the sake of it, it’s actually trying to connect the dots between previous transactions, current transactions, and potential future transactions.” In a survey by Oracle, over 300 C-level executives said their organization is collecting and managing 85% more business data today than it was two years ago. However, 47% of them said their organization cannot interpret and translate the information into actionable insights. While data is important, it is the right data that matters.

Stakeholders’ perceptions precede metadata ontology

A data entity might be consumed in different ways by different stakeholders. For example, while the telephone number field might be used by the sales agent to make customer calls, the tax analyst might potentially use the area code within the telephone number to get the tax rates as per jurisdictions. This means the actual use of the telephone number field is more than its intended use, thereby making metadata ontology challenging. In addition, in most cases the boundaries between data and information are not always clear. What is data to one person might be information to someone else and vice versa. To a crude oil commodities trader for example, slight changes in the sea of values coming from the exchanges might act as information for taking appropriate action. But to anyone else, they would look like meaningless raw data.

Data management is expensive and time-consuming

While businesses strive for quality data to derive insights, getting and managing quality data is expensive. Data is created, stored, processed, shared, aggregated, cleansed, replicated (to DR sites) and archived, and all these activities take time and money. According to the research done by Dr. Howard Rubin of MIT, 92% of the cost of running business in the financial services sector is related to data. Once the data quality is improved, the data quality must be governed in the entire data lifecycle, as it is estimated that data quality degrades at 7% per annum. So if organizations need quality data, then the data management initiative should be seen as a continuous improvement initiative at the enterprise level (not at LoB or function level) and not as a one-off project. Data management is a marathon, not a sprint.

Data might distort innovation

Data sheds light on the past events that no one has any control to change. Seth Kahan, author of Getting Change Right, uses the analogy of driving a car in data-driven decision-making. He says, “Making decisions just on data is like driving your car only by looking in the rear view mirror. During tough times, leaders tend to depend upon the past to make their decisions as they want to be certain about what they are doing. The more certainty an organization wants, the more they go backwards. But the past only shows where you’ve been, not where you are going or should be going.” According to Lara Lee and Daniel Sobol of Harvard Business School, “Data reveals what people do, but not why they do it. Understanding the why is critical to innovation.”

In today’s interconnected world, nobody makes decisions in a vacuum. As a result, data very much matters to business, and data is the fuel in which today’s enterprises run. But as explained above, there are also cases when investing time and effort in building a data-driven enterprise might be a futile effort or even harmful. So when data management initiatives are pursued, the effort could be more ineffective when the following occur:

  • There is no senior management commitment on valuing data as a shared enterprise asset (not at LoB or function level).
  • There is no enterprise-wide vision for running and sustaining a data management initiative.
  • The insights from data cannot be translated into actions quickly.
  • The relevancy of data changes constantly depending on time, space, and stakeholder preferences.
  • There is a need for unbiased details of the business processes and activities.

Thank you very much for your time. As always, I am sharing my thoughts to learn from my network. Let me know your thoughts and feel free to share this article in your network if you deem fit.

Regards!

 

Prashanth Southekal will be presenting a free webinar with ITMPI on May 26! Sign up here: Are You Ready for Your Journey to the Cloud?

About Prashanth Southekal

Dr Prashanth Harish Southekal is the founder Director of Catyeus (www.catyeus.com) an IT strategy company based in Calgary, Canada. He brings over 20 years of Enterprise Architecture, Information Management, and IT Portfolio Project management (PPM) experience from companies such as Accenture, SAP AG and General Electric. He has published 2 books on Information Management and is a frequent speaker/writer at leading conferences, universities, magazines, and industry forums. Dr. Prashanth holds PhD in IT Portfolio Project Management, MS in Information Technology, BS in Mechanical Engineering, and numerous industry certifications. He can be reached at prashanth.southekal@catyeus.com.

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