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Saturday 1st of October 2022

7 Data and Analytics Best Practices for Businesses


Effective Strategies to Manage and Analyze Enterprise Data

[Free Takeaway Inside – GDPR Compliance Checklist]

It has been predicted that by 2023 data literacy will become an explicit and essential driver of business value. There’s no doubt that data and analytics is key to many marketing functions to unlock actionable insights from raw data.

And yet, despite all the efforts, budgets and resources allocated to analytics, the vast majority of projects simply fail to meet expectations. According to Gartner’s research, only 20% of analytics insights delivered business outcomes through 2022.

This may be because most companies don’t have a solid strategy in place to make the most of analytics, operating instead from a mostly haphazard and unproven playbook. Besides, a lot of businesses hoard on data without putting it to good use.

“Analytics success isn’t just about data collection, it’s about data management and insight”

-McKinsey

Here are 7 best practices that top analytics experts recommend as the backbone for a successful data and analytics strategy:

1. Focus on Data Quality not Quantity

The lack of clean data is one of the main issues affecting most brands today, making it difficult for marketing teams to effectively target and engage with audiences. Marketers need to ensure that their data collection, analysis and activation process are optimized from the get-go.

“You need data quality and hygiene to be closer to where that marketing strategy is. There’s a lot riding on the quality (or lack thereof) of your data.”

– Martech Conference

Here are some data quality strategies marketing leaders can adopt to fix unclean data:

  • Create a data quality report that highlights potential reporting issues, including customer contact details changes, duplicate entries and profiles in need of suppression.
  • Conduct periodical data quality assessments and audits to ensure accuracy, completeness, consistency, timeliness, validity and uniqueness of data.
  • Improve data quality with hygiene and enhancement efforts

2. Bridge the Marketing-Technology Chasm

A new class of martech solutions is becoming central for marketing teams. Therefore, they have become more reliant on IT for activities such as site tagging, tracking codes, integration of disparate data sources and more. Yet, marketing and IT executives often don’t understand each other’s goals or roadmaps. However, both divisions need to work together with a shared vision to accomplish goals and ensure new technologies work.

 “From our perspective, marketing and IT departments will become even further blended.”

– Harvard Business Review

Creating mutual roadmaps to implement analytical projects and fostering two-way communication lines with both teams are some ways to bridge the gap between marketing and IT teams. Alternatively, companies can also look for a strategic digital partner to close the gap. These digital analytics strategy consultants bring a fresh perspective, offer technology expertise, emphasize customer journeys with user interface and experience design, and adopt a speed-to-market mindset with agile processes that will help bridge the marketing-IT chasm.

3. Implement Broad Range of Advanced Technologies to Support Greater Value Creation

Augment your digital analytics stack with advanced technologies that generate new use cases of data and act on it. Experts estimated that 80% of the current work done in analytics encompassed descriptive analytics — which provides a historical outlook of performance. However, marketers can diversify to AI-powered predictive and prescriptive analytical solutions as well that provide insights into the future and suggest the next best action.

“Data and Analytics leaders should choose analytical techniques that can use available data more effectively. Small and wide data approaches provide robust analytics and AI, while reducing organizations’ large data set dependency”

– Gartner

4. Build a Centre of Excellence (COE)

Successful analytics requires more than highly specialized data scientists working in silos. However, it’s vital to build a team of cross-functional specialists well-versed in their domain to collaborate and establish a framework for agile analytics methodology.  According to a survey, 60% of top-performing companies operationalized “a center of gravity” for their analytics efforts.

The COE typically consists of a specific set of roles, skills, and capabilities, including data scientists (“quants”), data engineers, workflow integrators, data architects, delivery managers, visualization analysts, and, most critically, translators from the business who act as a bridge between the COE and business units.

bridge between the COE and business units

5. Make Compliance an Integral Part of Analytics

Data is an enterprise asset, but can quickly turn into a liability if it’s not managed well. To counterbalance the risks, organizations must focus on compliance, including external regulations, internal business rules and industry standards. Compliance should not be an afterthought in analytics, but should be considered upfront across all stages in the data lifecycle and analytics project.

“Outcomes can’t just be good governance, Outcomes have to be running better businesses.”

– TechTarget

As data collection efforts ramp up, governance becomes a critical factor. Establishing a governance framework ensures data is captured and managed consistently, quality remains high, and there is a common definition and understanding of data across the organization.

6. There are no Silver Bullets. Small Steps Lead to a Huge Transformation

Large initiatives or programs can take a long time to deploy and rollout. And, there may be several inherent challenges in a large-scale implementation. A pilot application on a specific problem allows an organization to better understand the ROI of its analytics approach before scaling up to a larger solution. In this way small wins can quickly accrue as incremental gains making a transformational impact on your data and analytics strategy.

“Without those pieces in place, buying a solution and expecting an analytics strategy to emerge is much like buying a hammer and hoping a house will appear.”

– Gallup

7. Use Data Storytelling to Communicate Insights

Instead of generating reports with cryptic and confusing numbers or statistics, utilize the concept of storytelling to communicate insights using compelling narratives and visualizations. Data storytelling is more than creating visually appealing reports. It is a structured approach to generating actionable reports that uncover interesting patterns and outliers in the data.

“A study by Stanford professor Chip Heath found that 63% could remember stories, but only 5% could remember a single statistic”

Leverage visuals, context, and the financial benefits of data-driven insights to weave a story that educates and engages stakeholders.

Drive Action through Advanced Analytics

Many organizations are on the cusp of moving from conventional reporting and dashboards to experimenting with newer forms of analytics. The result is that companies are looking forward to a way to drive insight and action using analytics without becoming mired in data and infrastructure issues.

As organizations climb up the analytical maturity ladder, it’s imperative for them to follow best practices. These practices and strategies are rooted in core layers of data management, agile processes, collaborative discovery and automated operational deployment.


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