Top Challenges with Data Transformation

As organizations move towards being increasingly data-driven in their decision making, taking a structured and strategic approach toward data collection and data analysis becomes much more important in generating key insights for the organization. Our research has shown the four most prevalent challenges for marketers working on data transformation.

  1. Problem Identification :

It is common for organizations to focus on a technology refresh as a means for solving a problem. The comment we often hear is, “We have the platform in place, now lets see what problems it solves”. To achieve the best outcomes, it is always better to do the opposite. You may identify problems that need a genuine refresh to the existing platform, but sometimes, a simple set of additions are more than enough to arrive at a solid solution. The real goal becomes how quickly and effectively we can augment our problem solving capabilities and, instead of the tech fix, refocus on generating and applying key insights.

2. Capturing every single data attribute:·      

It is important to carefully choose the data attributes being considered as inputs. Having too many inputs can be as bad as having too few, and either can result in incorrectly identifying correlation and causation. An iterative approach best serves the effectiveness of adding more attributes to a dimension.

3. Data Preparation and Quality:

Most algorithms used for analysis are highly standardized with some minimal variances, with the delta almost always based on the data being fed into the platform of choice. The challenges here vary from consolidating data from various ecosystems to transforming and standardizing data. This is consistently one of the most difficult data problems to solve.

4. The Larger Journey

The fourth, and biggest, challenge we face for arriving at pertinent insights, lies not with the data we have captured but how that data relates to the customer journey. As marketing organizations seek to eliminate silos, we have a unique opportunity to re-calibrate the data strategy to better align with the new journeys, something that is unlikely to happen using older data sets.

How do we solve these problems ?

  1. Prepare the applications / journeys to capture relevant data

A strong transformation must always start with rearchitecting the journeys to cater to todays needs.

2. Define and baseline KPI’s

A strong and successful data strategy measures impact of any initiatives without bias. In order to do so, it is important to identify the performance indicators and baseline the values as well as the method used to arrive at the results. This will ensure that the same process is followed at the end of the transformation to show unbiased results

3. Define the data attributes and dimensions

The optimization of user journeys and processes presents a unique opportunity to realign the datapoints that form the basis of analysis and insights. Start small, verify your assumptions and models and tune it effectively.

4. Implement analytics to capture data

It is now time to implement the technologies needed to capture the data. Bear in mind that the size of the sample data captured for our data points determines suitability of the models that may be employed towards arriving at impactful insights. Of course, todays sophistication in technology really allows for crunching of massive amounts of data.

5. Prepare data for Analysis

The data generated may need scrubbing and normalization to identify anomalies and check if data is skewed towards a particular segment. As a rule of thumb you would spend 80% of the time pre-processing the data that the data science is applied on.  In many cases, it may make sense to generate synthetic data in order to complete the dataset. This is a very key step in the process. Ultimately the performance of the model is only as good as the data that is fed into it. This factors heavily into how valuable the insights that are generated from the model are.

6. Apply Data Science to generate insights

There are some excellent models already out there that can be employed. Choosing the model is dependent on multiple factors such as the problem being solved, the quality and quantity of data etc. Start with a simple data model such as logical regression to unearth quick insights before proceeding to something like neural networks. Also, the very nature of data science precludes the fact that the results are “inexplicable”. However, many organizations are aimed at democratizing data allowing for auto choice based on the data as well as allowing for “proprietary” additions and tweaks to a standardized model

Apply insights to your business

Properly resolving these issues will finally show the amazing outcomes that being truly data-driven can provide for your marketing efforts and show the real impacts on incremental revenue from campaigns and branding. It will also lead to more meaningful personalization, deeper engagement with customers, and increased customer value.

At TAOS, we empower your marketing team to focus on creativity, insight and innovation, while we enable you with the best marketing tools in the world.