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MioTalk X Korey Lee: Building a Culture Around Data

In this article, MioTech speaks with Korey Lee, Head of Data at SCMP on the effective ways on building a strong data culture from scratch.

Korey Lee, Head of Data, South China Morning Post2018-12-11

1. What is a data culture?

A data culture is shaping an organization to prioritize data an important variable in decision making. This requires training, education, and perhaps most importantly, building relationships with each business unit to determine which metrics will drive their decision making.

2. Why is it increasingly important to build a strong data culture within a company?

As the South China Morning Post goes full speed ahead in its digital transformation, one of the key challenges was to accelerate operational efficiency. While the data team has been built from the ground up in a little over 18 months, one of its core reasons for existence is to help every part of the business get better at one they do. We’ve established a culture of OKRs, Objective and Key Results which empowers each department and team to align with company level goals and have specific, measurable, actionable, relevant, and time-bound (SMART) goals.

A data-driven culture helps everyone within the company to have a consistent pulse on how they’re tracking to these individual, team, department, and company goals. My team checks in monthly on each of their quarterly goals which build accountability and transparency. If we’re falling behind, people are better able to ask for help or collaborate with other individuals or teams on shared interests or initiatives.

3. What specific benefits/examples can you cite where data has significantly added value, editorially/operationally etc. ? What pain points did having data solve?

In order to build automated digital metrics reporting and analysis, the team designed, constructed and implemented a centralized data warehouse and ETL processes. This enabled the aggregation of data from over 40 disparate data platforms. We also trained non-technical team members to use SQL and other data processing tools to automate workflows and report generation processes freeing up over 75% of one headcount. This, in turn, enabled a recalibrated focus on developing insights, recommendations, and action items, eradicating time-consuming and tedious report creation.

In order to ensure this transformative process was being leveraged across the organization, we scaled cross-functional business intelligence. Moreover, the team also consolidated redundant tools and platforms, resulting in the elimination of duplicated manpower, resources, and operational costs.

4. What active steps do you think is important for a company to take in order to build a good data culture? (education etc)

Perhaps the most critical component of driving business process efficiency is training, educating and evangelizing why data matters. The story-telling process requires the cross-pollination of data tools and culture to shift decision making from intuition to data-driven. Weekly online editorial meetings, slack, and email alerts for under-performing / over-performing content, daily insight emails summarizing wins/losses, and dynamic TV dashboards all contribute to driving this change. This helps to form rhythms encouraging data reliance on real-time reporting. Key performance indicators (KPIs) enable teams to better track progress and have increased accountability for targets; dozens of training workshops were held to ramp up staff on tools, features and tie data relevance into each individual's’ day-to-day. As the organization continues to transform, we’re encouraged albeit kept quite busy by the 10x increase of data requests over the past 3 months.

With better tools and more responsive ad-hoc analysis via Tableau, BigQuery and Airflow, we’re building a stronger data culture at SCMP, empowering business leaders and managers to access real-time data and make more powerful data-informed decisions.

We believe that creating a transformative culture starts with investing in our people. The impact was particularly apparent in our data-driven transformation, democratizing data via various tools within the organization, empowering our teams to make smarter decisions. Measuring the impact of those choices gives us the agility to adapt quickly when necessary. With the advent of these initiatives around data storage, centralization, and automation, we've seen massive gains in efficiency, growth in our core metrics (traffic, revenue) and a rising tide of excitement, morale and engagement at the South China Morning Post.

5. What barriers did you face building a data culture? (Mindset? Trust? Hiring?) How did you overcome it?

This is very similar to question 4 and I think we answered most of it there already.

6. Should data drive decisions? What's the right balance?

While data is not usually the sole variable in driving a decision, it should play a role in most decisions. I prefer to call this data informed decisions. Throughout the course of the past two years, we’ve seen an increasing trend in business leaders asking for and considering the data piece before jumping to conclusions or making decisions which is a big step from the “gut-feeling” or “intuitive” driven decisions that were more the norm previously.

A good example of this is A/B testing headlines in the newsroom. Sometimes editors have differing opinions on which headline works and which doesn’t, the best way is to put it in front of our users and see which one they click on the most, then regardless of which one we might like, we know what works and what doesn’t. The US-China trade war content has been a hot topic as of late and several headlines were written in the British style with Trade Row (Row as in a fight or brawl). However, since our audience is largely coming from the US now, Trade War picked up much more traction as Americans generally read “row” as “row, row, row your boat” not as “fight”.

7. How readily available should this data be to employees? How do you ensure that the data is actionable?

In an ideal data world, each and every employee should have access to the data they need to do their job. Naturally, security protocols need to be in place to ensure that individuals don’t have access to data that may be sensitive (e.g. PII - personally identifiable information) or data they don’t need access to because it’s irrelevant to their job.

Actions are typically tied to decisions. Spending time with the teams to understand their workflows, projects, and initiatives enable us to put metrics and KPIs in front of them that help them to make decisions and therefore are actionable. It’s also an iterative process, if something is no longer useful or we can make it better, we continuously seek to improve how our customers are engaging with the data.

8. What would be in your opinion, the best data culture?

In many ways, all of the things we just talked about. Aspirationally, an environment where everyone has the knowledge and ability to access to the data they need. Each person across the company should understand the value of data to their team as well as their individual work and be able to have self-serve access to actionable data to execute on their day to day work. Data tools should be largely automated and the data team’s focus should primarily be focused on generating insights, enhancing recommendations, and building tools that employ AI, ML, and algorithms that help drive the business onwards and upwards.