7 Best Approaches in Implementing Data Analytics for your Company
Before an organization transforms into a data-driven enterprise, you need to incorporate your business intelligence (BI) tools with smart data analytics approaches to actually create business value.
Regardless of their operational scale, SMEs and corporations work hard to make sure their data analytics initiative has the structural design in meeting specific business goals and withstanding inopportune slip-ups in the process.
In a recent article from Gartner, an analyst emphasized the importance of capitalizing on data analytics today. ‘Now is the time to anticipate, adapt and scale the value of your D&A strategy by monitoring, experimenting with or aggressively investing in key D&A technology trends based on their urgency and alignment to business priorities,’ says Rita Sallam, a distinguished Gartner VP Analyst.
CFOs and other C-level executives are enthused to discover how data analytics can impact the business in various areas. Combined with their commercial prowess and understanding of the current business challenges, here are big data analytics techniques to wind up in a scenario that’s favorable for your end.
What are the best business analytics techniques and practices?
Keep everyone tied up
Like any other business schemes, everyone involved should have an awareness of what’s happening, what the next steps will be and what they should expect moving forward. This could mean evaluating and defining your key performance indicators (KPIs) that are meaningful for your business. By establishing your key metrics on the get-go, you already set the direction of the course, albeit it can still change along the way.
For the management-level heads, their role is to establish an atmosphere of support for the data analytics project. Their involvement encompasses building and aligning key performance indicators (KPIs) under a mutual agreement to level the expectations and what the company needs to achieve.
Your employees are part of the process much as the higher-ups are. Aside from the prior things they handle in the data analytics project, it’s essential for them to understand how their role fits in the bigger picture. How will this affect them in the long run?
With all your stakeholders on the same page, you ensure that the company is working towards one business objective.
Data storytelling is a must
Now that your entire organization is in active participation in your data analytics initiative, you need to be certain that the insights you extract from your raw data sets are digestible, especially for those who do not have a background in data science or are new in business analytics. If your data appears obscure or indecipherable, it muddles the course — leaving key decision-makers in the balance.
Once data is organized [according to how it’s needed], this is where data storytelling comes in. All visuals and contexts presented should contrive to weave a narrative that elaborates the financial benefits to your stakeholders. Through data storytelling, the correlation of your drawn insights to the financial returns it brings to the company is recognized and well-accommodated. Without it, you risk your people losing interest in the business analytics project.
Remember that while it’s great that you have tons of data to back up your decisions, what you make use of it is just as important.
Leave room for inconvenient results
As you go along this process, there will be results that may not be as ideal as you expect. Digging through data breeds transparency, that's why outcomes of all sorts can be exposed — from the sensical to the negative ones. But even if the outcomes are not on your side, these don't automatically imply that your company's performance is subpar.
Inconvenient business insights challenge the conventions of an organization. They shed light over pertinent issues that can knock your practices, suggesting a new path that leads you to a bigger picture.
Disregarding such inefficiencies is a way of underestimating the power [and value] that business analytics yield in your enterprise.
Start small first
Another important data analysis approach that you might find useful is the practice of starting in small parts first.
Before you fully immerse your operations, try to aim for a small area with a significant role or a key issue where you could inject data analytics as a solution. Assess if it makes a difference. Did it leave an impact to that particular facet? How long did it take before it generated valuable results? Use the outcome to introduce the approach’s credibility and to build momentum before you apply the initiative on a bigger scale, until it’s implemented company-wide.
Use a toolkit
To help you further, there are also business intelligence (BI) tools that you can tap for easier data preparation and insight extraction.
Build a toolkit with software and applications that would supplement your existing process, or the one flexible enough to scale with your team through the project. In addition, you can also find ones that have built-in mechanisms to sort through your data and create different visual representations such as graphs and charts.
If you’re fairly new to this, there are analytics tools that have self-service features, making it easier to learn the algorithm and eliminating the need for coding skills.
CFOs, CIOs (chief information officers) and other business heads should align with their analysts to identify which problems and/or areas need immediate attention. Evaluate the concerns in the team and in your processes. What issue should be addressed soon? If it affects your team’s performance, consider putting it on top of the list.
Knowing your priorities would come in handy, especially if you have a lot to address. This way, 100% of your efforts would be channeled into every business facet that you need to get your hands on.
Build a BI team
Your dedicated analytics team will be the asset that brings your analytics initiative to life. Through your team of analysts, you have the appropriate expertise and skills to foster a data-driven enterprise, shying away from traditional methodologies that only relied on hypotheses and guesswork.
Through the competencies of your analytics team, you also push the boundaries of data analysis beyond finance and accounting or regulatory reporting to involve your operations and revenue growth.
In making your data work for you
Business analytics isn’t confined within the likes of corporations and large names in the industry — instead, it’s an empowering solution for organizations, big or small, to make a significant impact and gradually grow in a sustainable [or accelerating] way. Through these approaches, you ensure that your data analytics become fully embedded within the business, the operations and your processes.
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