#9 – Key factors to consider when implementing a data strategy

Amy has a track record in software engineering and progressed into a Data Advocate with a focus on Data Strategy and influencing the Businesses overall usage of clean actionable data.

We spent time with Amy to discuss all things data from data strategy to data cleansing to stakeholder management.

What key factors do you need to consider when implementing a data strategy?

I would consider 5 different factors.

1. Current and future business needs

Develop an understanding of where you are at and what the future needs are
It will allow the business to grow faster and you can leverage momentum

2. Understanding the analytics team

Evaluate current staffing, what new hires need to be added and what skills you have within the team
Important to see what current skills you have to help you hit future needs

3. Current processes

An area that is often overlooked and is vitally important
Examining everything you do with data in each team or department
How you manage the data or overall processes to ensure efficiency
Are we using data silo’s/how are we collecting data

4. Data Accuracy

Understanding from data cleansing.
It’s best to update at the source if possible via ERP, CRM etc
Sometimes it makes since to mask the data instead of updating it millions of times

5. Business relationships,
Processes are only as good as how the people are willing to adapt to them
Building relationships at every level, helps data cleansing.
Important to keep all parties involved and engaged.

What are the main challenges you face when implementing a new strategy and best practices?

People don’t always like change so it is one of the biggest challenges we face.
Lots of teams have great individuals but sometimes they still struggle with change.

Its important to help the team understand the benefits for change and to keep them part of the movement

We replaced scripts for data virtualisation as we have to look to the future and sometimes work several months ahead
Ultimately it is worth it in end as it makes the team faster and more efficient.

In regards to Data validation and data issues, its an over changing process and it does open up risk from various data issues.

VINTAGE: How important is Data Cleansing in relation to Data Management and Data Analytics

Data Cleaning should be part of the foundations of any data strategy.
It needs to be implemented in the early stages and a team cant do reporting until data cleansing is in place.

It helps streamline data managements and can be adjusted at different levels

Often team members have technical expertise but not the stakeholder management experience.
How do you educate and train members to liaise well with stakeholders?

First, it is important to try and figure out what stakeholders are thing and how technical they are.
Vital to understand who your audience is so you can adjust your communication style.

We encourage good listening skills and the ability to adapt to different personas.

The technical side is more challenging as it comes down to terminology used and how technical you need to be

We provide ongoing coaching around approach and email content too when communicating with stakeholders

Reoccurring meetings are really important to continuously get the message across and keep it fresh in peoples minds.

By offering this support, it demonstrates that you care and all parties are happy.

What podcasts, articles, blogs do you follow?

Digital analytics power hour podcast. Fun and light and lots of interesting topics
Follow people on LinkedIn who post good things, not generic topics as I learn from new content.

I like variety on LinkedIn around data analytics mainly.

Delighted to be able to discuss at length all things data related. Thankyou Amy!

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