The growing complexity and speed of data available to businesses today should make decision-making easier. While the explosion of data triggered major advances in data engineering, storage, and computing, it also brought new challenges.

There’s been a big wave of innovation focused on reducing data latency. Many companies are able to collect data in real-time but analyzing it for insights still takes days or weeks, while business stakeholders impatiently wait.

Why is this a problem?

Business can’t wait. To thrive in a world that is changing faster than ever, business stakeholders need quick answers to performance changes. Data is only valuable when it is actionable, so the analysis and insight creation shouldn't be playing catch up with decision speed but bring agility in the decision-making process.

In this context, a key analytics metric is decision latency.

Decision latency  is the amount of time it takes for a team to make a decision in response to a business change . In other words, time to actionable insights*.

*An insight is more than an observation. It’s a finding that triggers teams to act. And for it to be worthwhile, it needs to be found before the window of opportunity is lost. An increase in decision latency causes missed opportunities and creates a setback. (read further here)

 

Why are most analytics teams so reactive?

Most teams are mainly reactive, missing the window of opportunity to drive business impact. Common symptoms I see that create a snowball effect:

• NEW CONTEXT: With more data available than ever before, data teams find themselves facing a new problem: not knowing where to look in the data and where to focus their attention.
• PEOPLE/CULTURE: Traditionally data and business teams worked separately but the new information age demands close collaboration. Analytics teams that are disconnected from the business lack the domain expertise/business context to identify top priorities and don't know what they should proactively look into.
• PROCESS: Many teams try to implement a “self-serve” model but most fail to surface insights and business teams depend on analytics teams to get them. However, there isn’t a structure/consistent way to collaborate. Analytics teams mainly respond to stakeholders' questions/requests (ticketing systems, Slack messages, etc).
• TOOLS: Teams are working with BI tools that are designed for the data amount and complexity of the past decade/s and losing a lot of time on manual slice and dice. Additionally, it’s still on the data organization to prepare and maintain the data, which often requires custom efforts for each new metric or project.

All leading to a direct negative impact on the business, undermined data culture as well as frustration and attrition.

Negative consequences of a reactive approach

Time is money. So, a reactive approach has a direct negative business impact. The companies failing to act on opportunities on time are unable to drive outcomes. Moreover, decision latency has a lasting impact on the data culture and often leads to a downward spiral with dire consequences such as attrition.

1. Direct business impact — The missed window of opportunity

A proactive approach helps teams prevent negative outcomes and maximize performance. Whereas with a reactive approach, at best you can hope for is to minimize losses. Hence every minute/day the answers aren’t found could mean a decrease in profits/market share or loss of customers.

One example I’ve heard this week: the active user base was declining for 2 weeks when the growth leader asked the analytics team to look into this. After 1 week, the team found out that a competitor was running an ad campaign for a specific term that was taking away significant installs. By the time the team found these insights, it’s been 3 weeks and the estimated business impact is worth millions! What if the team would proactively explore this after a couple of days?

Imagine how many smaller changes go unnoticed… Furthermore, if your teams are doing what can be automated manually, it is a waste of resources that can be allocated to value-added tasks.

Outcome: significant missed opportunities with a direct impact on the company’s bottom line, losses due to inefficient use of data teams

2. Undermined data culture

Delays between business questions and data-driven answers undermine the data culture of many organizations. If insights are often delivered too late, business stakeholders will adjust their expectations. In practice, they will start neglecting data inputs and start making decisions without data.

Outcome: undermined data culture as stakeholders don’t expect to have data insights at the speed of business

3. Downward spiral leading to attrition

A reactive data team usually falls into a downward spiral:

• Ill-defined requests and constant follow-ups (e.g., the typical “quick question” over Slack)
• The team is overloaded with so many requests
• The data team is detached from the business and doesn’t see the impact of their work
• Highly dissatisfied and overworked teams

Outcome: downward spiral affecting analytics team’s motivation and performance, potentially leading to attrition

 

Ideal scenario

Best-in-class analytics teams proactively influence business decisions on a daily/weekly basis. They don’t wait for questions or requests from business stakeholders. Instead, they are strong business partners and proactively share relevant insights and recommendations.

Maximizing the value of data requires proactivity. And not taking action till you observe a sharp decline in your dashboards and then spending days on root-cause analysis can be very costly.

Proactive teams can:

• Deliver insights at the speed of business
• Anticipate the questions they might receive and prepare answers beforehand
• Become a trusted business advisor powered by data

As a result, these teams push the boundaries of data culture and literacy, maximizing the business value of data & analytics. A couple of examples I’ve come across throughout my work at Kausa with some of the most data-forward organizations:

• Teams rapidly test and implement new technologies to augment analytics and create agile workflows
• Data and business teams come together for daily/weekly business reviews with specific hypotheses/recommendations as to why things are changing and what to do about it (what + why + so-what)
• Data teams are the ones to raise questions to the business (e.g., I’ve noticed this is happening for 3 days, I looked into the data, and… what do you think?)

 

How to accelerate speed to actionable insight

A cultural shift and backing from leadership are critical for this transformation. Given that this challenge is triggered by technology, technology also holds the key to overcoming this challenge. Augmenting analytics workflows can speed up manual slice and dice by ten times and help teams to get out of reactive work cycles by offering comprehensiveness and speed at the same time.

Promote a cultural and mindset shift

Data teams need to partner closely with business stakeholders to:

• Define business goals and priorities together
• Define speed to actionable insight as a north star metric - e.g. setting goals like answering 95% of the questions within one hour or accelerate time to actionable insight by 75%
• Promote a data culture where teams don’t wait for questions/requests from the business
• Reduce fire-fighting by developing good processes, automating as much as possible, and preventing issues.

Augment traditional workflows

Achieving speed and comprehensiveness together is key to taking proactive action. Hence teams need to:

• Leverage augmented analytics solutions that use ML to augment current workflows and automate repetitive tasks
• Test all the possible drivers affecting metrics daily and develop alerts when something unexpected happens (Read more on how to augment analytics workflows here)

Embrace data products

Teams can trigger a shift from Data as a service to Data as a product mindset by:

• Managing all data assets as products
• Embracing a decision intelligence product for diagnostic analytics vs approaching in an ad-hoc/reactive manner to reduce inter-team dependencies and establish a real self-service culture.

 

In a nutshell

A reactive approach to analytics can damage your business both monetarily and culturally. To avoid and improve:

Watch out for the symptoms:

• Teams losing focus and growing gap between data and business teams
• Inefficient processes and treating data teams as a ticketing system
• The long time between questions and data-driven answers
• Teams don't share any insights proactively, but rather just react to requests and questions

Communicate the consequences of reactive analytics:

• Negative impact on the business
• Undermined data culture
• Increase in attrition

Start accelerating time to actionable insights by:

• Promote a cultural and mindset shift
• Augment traditional workflows
• Embrace data products
• Create leadership support to drive a cultural/mindset shift