Too Much Data? 4 Ways to Shift From Data to Insights

Although big data was widely welcomed as the answer to every business-related question, the reality hasn’t been quite so simple. It could be said that today’s enterprises are suffering from a surfeit of data, but a lack of insights.

Businesses today gather data on literally everything, from social media mentions to the Google queries that brought visitors to your website, the precise GPS coordinates of your supplies and deliveries to the relative popularity of different colors of T-shirt, and including inventory levels and conversion rates along the way.

They say data is the new oil, but just like oil, data has to be processed before you can use it as fuel, and many companies are struggling to achieve that. Floods of new data come in every hour, but decision-makers still don’t have the actionable analytics they need to optimize processes.

Here are 4 ways for companies to bridge the chasm between data and analytics, and ease the process of generating meaningful insights that drive grounded decision-making.

1- Enable self-service analytics

When every query has to go through your data science team first, it creates a significant bottleneck that prevents teams from accessing the insights they need, when they need them. Instead, find an easy-to-use analytics or business intelligence (BI) interface that doesn’t require technical expertise, making data insights accessible to all your stakeholders.

Consider introducing data warehousing, which decouples your data source from the compute architecture to make it easier to run this kind of self-service analytics. It’s faster and more scalable so it can handle any number of queries, enabling you to open up self-serve analytics to all your departments instead of setting your data science team as gatekeepers to prevent the system from becoming overwhelmed.

It might leave you trying to choose between Redshift vs. Snowflake, but whichever you settle on, you’ll free up your DS team to spend more time on high value analytics projects which will have the long term effect of further improving access to analytics insights.

2- Integrate all your data sources

Another difficulty that comes along with today’s glut of data is that the information is coming in from so many different sources. It’s easy for silos to spring up between datasets, leading to data fragmentation which can harm data quality, undermine your ability to create a complete and well-rounded understanding of the topic at hand, and throw up obstacles between you and the insights you need.

Use a cloud-based data repository to integrate all your data sources in a single location, ensuring a “single source of truth” that you can rely on for trustworthy insights. Fragmented data is often poor quality data, because there may be crucial elements that are missing. An integrated data system helps ensure that all your insights are reliable, up to date, and accurate.

A cloud-based system also allows you to constantly update all your data streams, as well as import data from third party systems, so that you can achieve a truly comprehensive understanding of customers, operations, and/or campaigns.

3- Begin with your goals

It’s tempting to gather data first and ask questions later, but the risk is that you’ll become paralyzed by the data flooding in and feel unsure about where and how to begin. Enterprises achieve meaningful insights faster when they approach data gathering and analytics with specific goals in mind, like increasing conversion rates, shortening sales cycles, or unifying supply chain monitoring.

This way, you’ll know which datasets to select for from the very beginning. When you can prioritize the right data points and sources, you’ll end up with all the data you need without getting bowled over by too much of it.

4- Automate data preprocessing

Poor quality data is a serious handicap in the race to reliable insights, but cleaning, deduplicating, and verifying your raw data can be time-consuming and tedious when you do it by hand, significantly slowing down your analytics processes.

Instead, invest the time in building and training machine learning (ML) models that can automate data preprocessing, helping ensure that your data quality is high and your insights are trustworthy, without dragging down the entire system.

Too much data doesn’t have to drag your system down

Big data can transform your business operations, as long as you put the systems and strategies in place so you can surf the data tsunami instead of allowing it to crash over you. By integrating datasets into accessible repositories, automating data preprocessing, and setting a purpose for data acquisition, you’ll be better placed to ensure your data serves you instead of the other way around.

Final Thoughts

This data is truly a treasure trove of information that can help you forecast sales, predict customer demand, and mitigate risks before they materialise, but only when you succeed in converting it into insights. It’s all too easy to get overwhelmed by datasets and find yourself unable to see the wood for the trees. Follow the above steps to make better data-driven decisions to maximize your business profits.