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Convergence Partner Network: Axian

Axian – The Data Lifecycle: Enabling Business Growth

By: Shawn Duffy, Axian Custom Software & Data Solutions

The Convergence Partner Ecosystem

Axian, Inc was selected to join the Convergence Partner Ecosystem for their expertise in the areas of SharePoint and Business Intelligence solutions.  Their team has provided business critical solutions to companies big and small here in the Northwest.  Convergence was strategic in picking partners who share similar company culture, values and strive for excellence in customer service throughout their daily business activities.

“Big Data” & Analytics

Everyone is talking about data, “big data” and analytics. Both the volume and sources of data are proliferating at light speed, particularly with the advent of Cloud infrastructure and the Internet of Things (IoT). Companies, small and large, individuals, and inanimate objects create, save, and utilize data. But is there value in that data and what does that mean to you and your business?

To help simplify the answers let’s introduce a concept known as Data Lifecycle. First, Data is created or generated. Then it can be aggregated and synthesized into a database or data warehouse for easier access and analysis. Upon analysis, key information can be harnessed and utilized to have a positive impact on every part of your business. Finally, data can be archived and preserved – in many cases it must be. There are numerous smaller elements and subtleties to any data lifecycle.

Axian: The Data Lifecycle

Dude, where is my data?

Collecting, organizing, storing, and analyzing data is becoming more complex for businesses as each day passes.   With hardware limitations now being removed, via Cloud, IoT, and other advances, the volume of data is ramping up exponentially. High quality data and analysis are now proving to be immeasurably valuable to the growth and profitability of organizations. Optimizing operational efficiencies, improving targeted marketing and sales efforts, and maximizing customer experiences are just of few of the tremendous values of effective data analysis.

From Amazon suggesting things that you might like to purchase to Facebook’s hyper-targeted and personalized ads, detailed data utilization is everywhere. Where does all this data come from? Well, standard business activities from producing product to selling to customers to processing of orders can all be found in your traditional in-house systems such as MRP, ERP, & CRM. Many forms of manual data entry add to the stack. Additionally, social media, market research, & customer feedback provide sources of information that can and should be coalesced into your analytic framework. And more recently, data is captured from devices and sensors that measure everything from consistency of manufacturing processes to the amount of energy your refrigerator is wasting or when you need to buy more milk.

Now what do we do?

Now that we’ve sourced and conceptualized our data, the next step is to capture and gather it.  Collection can often be the most challenging step in this process, as some companies like AC Nielsen, Hoovers, and Kantar will provide you with great access to market research data, but for a steep price and often in forms that aren’t extremely conducive for all data load processes. Secondly, the flood of new information can often produce the additional challenge of separating the information from the noise, but such is unstructured data.

Axian - The Data Lifecycle Example

Figure 1: My Eclectic Amazon Recommendations

The goal of gathering the data is to ensure it can be organized, accessed, and utilized. This is where ETL and synthesis processes come into play. The key here is not to over engineer the connections between data and your business, rather loosely couple them so as not to stifle the creative process that is data analysis. The challenge is mastering the balance between viable, tangential connections and restrictively concrete relationships of data. Remember that for some discovery processes, directionally accurate indicators are just as valuable, so channel your Calculus II class and image your final solution is “Approaching Positive Infinity”…aka it’s going that way!

The data also needs to be held somewhere in a secure, yet accessible fashion, such as a database, probably supported in the Cloud.

Answers without Questions

Recapping quickly, we have sourced, captured and loaded the data into an organized warehouse. What’s next? Now, it’s time for the rubber to meet the road in the form of discovery and analysis. This can take so many forms that we can’t possible cover them all, but a high level set of categories can include:   operational, ad-hoc, and predictive analysis. The operational analysis is quite simply the analysis necessary to become more efficient at your daily activities, i.e. keep the lights on. This can take the form of a more efficient production line or sales closure rate or even financial month-end close. Ad-Hoc on the other hand is the more stream of consciousness analysis that allows Business Analysts in your organization the free-form discovery that should drive high-end efficiencies and hopefully some innovations. This would be things like reduction of scrap/loss rates in your manufacturing processes, or a new methodology for engaging with your customers, such as a tailored message to each based on their order history and web browser traffic, etc. Finally, predictive analysis is the forward looking methodology necessary to anticipate where your market and customers are headed, to anticipate their needs, and to get there before your competition. While this is a bit like reading tea leaves, your newly loaded data sets should allow you to better analyze industry trends, social media spikes, and customer behavior. When overlaid with your internal metrics, these can be quite powerful drivers of business change and profit maximization. In aggregate, this is what is meant by Business Intelligence.

Do you liked your cheese aged?

Finally, we must pay careful attention to what data needs to be archived long-term, repurposed for future use, or purged completely. Some data, like medical records and vital statistics should be kept for longer periods of time. Other data, often overly identifying or redundant, can be purged. Generally, all archived data can be repurposed back into the analysis and discovery stage of the data lifecycle.

Conclusion

Data’s prevalence in business is a long foregone conclusion. Most of us are just looking to maximize the value of our existing data to help keep the lights on. But as new data sources emerge, we have tremendous opportunity to improve in every aspect of life and business. The data lifecycle allows us to put together a framework for how data can be of value to your business. Data is everywhere, in every process, transaction, and function; but, the ability to collect, process, and analyze it can make the difference between business vitality and dying on the vine. A big question remains: “Where do I start?”

Taking Action

Don’t be confused by terms such as Big Data and Business Intelligence. If you have data, it is big to your business.

Axian Meme

Are you struggling with a complicated ERP system? Data consistency? Messy spreadsheets? Axian can help you grow and improve your business. Axian delivers clarity and value to your organization with easy-to-use data software solutions. To learn more contact us at 503-644-6106.

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