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The reasons why companies continue to make the wrong data-driven decisions

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The concept of big data has been around for over a decade. While most organizations have focused large amounts of resources towards collection, visualization and analysis of data, few have truly taken advantage of it.

Companies today generate more data than ever. Data, that can give them the opportunity to gain insights into consumer preferences, behaviour, product lifecycle, supply chains, marketing and more. Despite this, many organizations continue to rely on the gut instinct of their executives to make decisions. The few that do rely on data aren’t optimizing opportunities to drive growth. There is rarely a single reason why organizations fail to reap the benefits of their data platforms, but there are three themes that are repeatedly observed - analysis paralysis, organizational silos and fear of failure.

Data can lead to analysis paralysis

An abundance of data can easily become the very roadblock to decision-making that it is meant to remove – overcomplication leading to inaction from the people responsible for growth. While there are a number of reasons why data can create paralysis, here are a few that are most common:

Not identifying data-based objectives

What decisions need to be made through the analysis? Companies race to capture data and analyze it without a clear end goal. A solid data strategy tied to business objectives is key. Data strategy needs to focus on measuring the progress of a project, solving a problem or gaining insights.

Unstructured data capture

A problem with multiple sources of data is that it invariably provides varying information, making it hard to know which source to rely on. One solution to this would be to remove the clutter. Eliminate data streams that have the least effect on decision-making and focus on the ones that help generate results.

Not differentiating between big and small decisions

Data capture and analysis can help make large decisions that shape the direction of a company or smaller decisions that enhance a product or fine-tune a service. Expecting the same types and volumes of data to inform every business decision can result in paralysis. Give each decision the right amount of data and analysis time it deserves based on its importance. Aligning importance can reduce effort put into smaller decisions and direct energy to where it is needed most.

Focusing too much on perfection

Perfection should never be the main driver to data-based decisions unless the decision impacts the future of the business. Making decisions based on data analysis should be considered a step in the right direction of a larger objective or goal. It’s okay to make smaller decisions that make incremental improvements. It also provides breathing room in case some of the decisions don’t pan out as expected. Data-based decisions are meant to be iterative, make a change and measure the results.

Lack of hard time limit on data analysis

Time is precious in today’s world. The problem with data is that it continues to accumulate with time. This makes it easy to wait one more week which can turn into a month or a year before any decisions are made. Set checkpoints with a regular cadence to force your hand; make decisions based on the information in front of you. While there are no hard and fast rules around cadence, referring back to your data strategy and setting the right strategy to learn from decisions can help decide the right cadence for each type of decision.

Analysis paralysis kills productivity. But, it is just one of the factors that can impede data-driven decision-making. Organizational silos also have an impact on data and analysis.

Business silos inhibit a complete view

Departmental silos within an organization are seen as a growing pain for most companies and are considered one of the largest detractors of innovation across the board. Unfortunately, silos in most organizations are there for a purpose, with the objective of focusing specialized talent towards very specific goals.

The problem with these silos is that they generally create isolated islands of teams rather than an organization of people working together towards a common goal. When it comes to data, the challenges are no different. Business silos within an organization generally create data silos, making it almost impossible to connect all of the data streams into a single view. It also hinders the ability of decision-makers to have a clear view of all of the important business data needed to make the appropriate overarching decisions based on its analysis.

The four most common ways data silos affect a company are:

Limited view and analysis

Business and data silos generally require each department or group to aggregate and analyze their data separately from the others within the company. Because of this, decisions are made within groups rather than at a corporate level with the big picture in focus.

Lack of integrity

Data aggregated by departments are generally stored separately, using different technologies, tools and providers. The challenge here is that data between departments will be inconsistent and very difficult to integrate. In addition, the data can become less accurate and, in some cases, may be out of sync when different teams collect similar data.

Duplicated resources

As data is being stored separately, the analysis of it is normally done separately as well. This leads to increased cost in hosting, analytics packages and labour because each department will need separate budgets to manage their own practices.

Lack of collaboration

Separation of data continues to drive a siloed corporate culture where each department will compete to ensure they have the funding to pay for the resources they’re creating.

The key to breaking down data silos is to create an organizational culture where business silos don’t exist. Unfortunately, it’s not always as easy to do as we’d like it to be. Another way to eliminate data silos is to create a unified data strategy where data and its analysis is centralized by a neutral group and then distributed to the teams who need to understand it and make decisions based on it.

Avoiding the fear of failure

Fear of failure is another major theme extremely common in most corporate settings and deeply impacts data-driven decision-making. It’s a cultural thing where failed projects are considered negative to career advancement. Because of the negativity, teams do everything they can to constantly show that their work is creating a positive impact within their sphere of control. The consequence of this type of culture is a safe attitude when it comes to decision-making. The potential fall out is a lack of creativity, innovation and progress against products and services, generating results that are counterproductive to business growth and sustainability.

Making data-based decisions is essential in today’s business environment, unfortunately, most companies still don’t see themselves as data-driven. A commitment to a data-driven culture that has a strategy and focuses on the openness of information and failure can give your company the confidence to pursue challenging and rewarding growth. It’s not always the easiest path to take but becoming more data-driven and less instinct-focused will only help your long-term business strategy.

Scott Wassmer, general manager of Americas at Appnovation

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