Why Your Data Analytics Doesn’t Work
Being data-driven is not enough. Here’s how to ensure data analytics success for your business.
Decision-making should be data-driven, not chaotic or purely intuitive – it’s a trivial statement. But ask yourself a question: are the decisions formulated after a thorough data analysis always leading your company to bigger profits, or have you just recollected at least one story when a blind reliance on data analytics systems and the conclusions they’re drawing cost you lost sales opportunities or extra expenses? Veronika Selitskaya, data analyst at Oxagile, brings to light potential reasons for failed data analytics and advises on what moves businesses should make to avoid poor results, even if they’re data-driven.
Over-reliance on data might play a low-down trick on you, so beware of stumbling blocks that might be hidden at all levels, preventing your data analytics solutions from contributing to your financial success. Let’s try to uncover all of them to never appear in a position when even a range of analytical tools in the arsenal combined with thorough data analytics workflows result in incomplete findings or wrong conclusions.
Typical pitfalls are based on a “3W” formula – Wrong System, Wrong Data, and Wrong Approach. What does each component imply?
Wrong Data: Data Quality Is the Groundwork
You will hardly cook a perfect dish without having high-quality ingredients. The same with data – before launching a data analysis process and generating data-powered business strategies, the company should carefully measure its data quality level and consider only complete data pictures, e.g., if some fields are partially empty, we leave them out of the analysis. Accurate data is more likely to favor business growth, provided a sound business strategy is undertaken.
So, what could be wrong with the data?
Discrepancy between the data used for analysis and actual business needs
When there’s no clear purpose behind data metrics chosen for analyzing trends and insights, reports are likely to be useless in the end. The understanding of business needs and expectations from data should be formulated prior to building a data architecture. Otherwise, you risk either coming to inaccurate conclusions or failing to arrive at them at all.
Case in point: A global telecom company’s regional branch with over 3,000 employees faced an overload of data reporters who were buried under the myriad of BI report requests from different departments, including Sales, HRs, Marketing, and Service. To optimize the work of data specialists and quickly grant meaningful reports to all end users who needed them, the company interviewed representatives of various divisions and found out what each company role expected from data. This knowledge became a foundation for reconsidering data aggregation principles and generating more targeted data sheets with the insights presented in a clear and structured way.
See more: How the Toronto Raptors Operate as the NBA’s Most Data-driven Team
Missing data
Utilizing incomplete data for modeling, let’s say, a possible demand across regions is a typical scenario when managers may go the wrong way and come to incorrect conclusions. It’s far better to devote more time to unearthing the right data and drawing the full picture of customer behavior than to take some strategic steps and incur substantial financial costs after banking on the wrong regions in your sales strategy.
Dirty data
No matter how experienced and skilled your data analysts are, they can hardly do magic, turning duplicate, erroneous, or outdated data into accurate data-driven forecasts. Having a thorough knowledge about available products and services or project specifics, data specialists are keeping track of any single inconsistency and performing data cleansing. While it works for moderate data quantities, how to combat incorrect data added to an extensive database by dozens of units? Automation is key to presenting significant time savings along with releasing you from tiresome, error-prone, and totally unjustified processes.
Tip in point: lay down data standards from the get-go
Do not underestimate a data standardization and mapping step – set master data, drop-down lists, and required parameters (ID, Country, Number of Employees, Annual Revenue, etc.) to reduce errors and redundancies in data across your data analytics system.
Is a mandatory field or value missing? The system will signal the blank spaces. Also, if different teams, roles, and departments enter the same info inconsistently, it won’t accept wrong data records.
Wrong System: Time to Weigh the Pros and Cons of Your Data Analytics Solutions
Now let’s look at the system of data analytics that you’ve laid out at work. What are the typical failures, and how can they be improved?
System and business simply don’t form a perfect match
How much data does your business possess? What do you need analytics software for? Ignoring these questions before the system selection process and relying on the solution’s reputation on the market and rave reviews can bring more harm than value to your business. You shouldn’t rate the software as below average only because you can’t seize even half of its opportunities.The real project example of how a multi-functional data analytics solution and clean data haven’t managed to work in tandem is below.
Case in point: when one of our clients considered the best option for data collection and analysis, it turned out that they had already implemented an advanced analytics system a while ago but had no idea how to make the most out of its functions, using it mainly as a data dissemination tool. When we were trying to unveil all the killer features of this system, the arising technical issues with data integration confirmed that this software had been a lost investment and that all its competitive differentiators hadn’t worked for this very company.
A tangle of data systems: how to unravel?
This request is common, especially among our media and broadcasting clients dealing with a handful of platforms. When live experiences are provided via both modern streaming devices and more traditional infrastructure like cable or satellite TV, the data on video content consumption are processed by various systems, which are often slow-paced and fragmented. Here should come a solution that unites separate tools into one data processing ecosystem and hence gives full-fledged analytics for the company.
When a system becomes old-fashioned
When companies are dealing with ten million data records and almost 10 GB of data daily, like AdTech market players helping brands maximize return on digital ad investment, their data processing tools considered to be flagship yesterday, today might become a bottleneck on the way to scaling a business. Postponing the replacement of rigid components with modern data platforms can negatively impact the speed and quality of data analysis.
What’s next? End customers are questioning the value they get, not to mention the absence of new clients coming to enrich a current portfolio. So, the sooner the digital media analytics representatives migrate to more flexible tools able to support even heavy data loads, the more benefits from the service they provide to the end clients.
Wrong Approach: What If Data Management Is Breaking Down?
Even if there are currently no issues with the analytics system’s settings and business logic, data ownership is equally important in the overall data management process. Word of mouth doesn’t work, especially in corporations, when it comes to knowledge sharing about the data sources available or algorithms and tools for collecting analytics.
To avoid being blocked by organizational matters relying solely on data analytics with necessary practical skills, a company should care about having and timely updating comprehensive documentation – data dictionaries and data lineage. On the one hand, this source of truth will protect you from the mistakes data analysts could make trying to look into the system specifics on their own. On the other hand, delayed data updates get us back to the data quality issue – old data will entail wrong conclusions.
Have you faced any of the discussed challenges in your approach to data analytics? What was your experience like? Share with us on Facebook, Twitter, and LinkedIn.
