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Your IT Company Must Help You Understand Data Analytics

Data Analytics is a service often offered by IT companies, but rarely fully utilized. This is a symptom of both IT companies and their clients not fully understanding how to take the insights of a data analytics program and put them to use. But in the client-provider relationship, who is responsible for understanding the services that are being offered as part of the value of your IT services?

Data analytics can be a powerful tool, accelerating your business forward with data-driven insights and quantitative evidence of success; but only if you know how to use it. A sheet of data is only useful to you if you can interpret it. Those interpretations are only useful if you can determine your next-step actions that give meaning to the insights revealed.

Is your IT company helping you to understand your data analytics? You might be surprised how often the answer is “no”.

Data Analytics

Data Analytics is Not a Gift Basket

All too often, data analytics are offered to a company like a gift basket or a cookie bouquet. “Look at all this data!” you are encouraged to think. It’s so potentially useful. It is presented on such colorful graphs. But in reality, data is only as useful as its applications. If you can’t understand and take action on the numbers coming in from your data analytics trackers and algorithms, your dashboard might as well be a screen saver.

Many brands today have extensive data analytics tools at their disposal ranging from Google Analytics to stats offered by their current business software – they just don’t know how to use it. Some don’t even realize the data is there for the taking.

When your IT services provider presents a dashboard of analytical data to you, the first thing you should as is “How do I use this? How can I put this data to work?”

What Is Data Analytics, Really?

Data analytics is the collection and study of vast operational data. Data analysis is specifically the study of data to draw conclusions. This is a broad definition because there are so many ways that data analysis can be applied. In marketing, your data analytics may tell you about customer behavior; which pages a customer visited and which elements they clicked on. Manufacturing companies take analytics from the runtime, downtime, and queues of machines. Fleet managers handle analytics for route speed, fuel consumption, and even the acceleration rates of each vehicle.

Data analytics is simply taking the data already handled and processed by your company and drawing insights from the mass. The right data analysis can reveal patterns, trends, and risks that may help shape your following decisions to better streamline or secure the business. You can use data to improve customer experience, sell more products, determine which tactic is most effective, and analyze how different factors influence each other in a connected system. Data analytics can guide long-term strategy and short-term decisions.

But to make the best -or any- use of data analytics, you need to understand your data and to process it in a useful way that produces valuable insights.

How Data Analytics Should Be Helping Your Business

  • Business Decisions
  • Marketing Results
  • Website Design
  • Cybersecurity
  • Internal Efficiency

Informing Business Decisions

Data-driven decisions are at the heart of business growth today. Why make off-the-cuff decisions based on your gut when the data can often tell you exactly which direction is the right one to take? Data can tell you when to extend and when to reinvest in the company. Data can tell you where customer sentiment and buying trends are leading. Data can inform you of trends in the industry and internal data analytics can tell you when to expect bottlenecks or hire temporary workers.

Your IT company should be helping you see these details on the horizon spelled out by the big data that flows through every modern business.

Honing Your Marketing Results to Perfection

Marketing and data analytics have gone together since the practice was done on printed spreadsheets. How much did this ad-spend increase impressions? How much did that campaign increase sales of the featured product? Which landing page gets the most results? Which campaign appeals more to your audience? Can you split your audience into cohorts and market to them individually?

Data analytics can help any brand hone its marketing results, refine marketing spend and find that perfect balance of customer hand-holding and self-direction. But only if your IT company makes this data available in a way that provides useful and adaptive marketing insights.

Streamlining Website Design and Customer Experience

Your company is the hub of your customer-facing data. The digital environment is yours to track and refine, meaning you can collect every crumb of data regarding customer journeys and how they use each page, then compile it into some incredible insights. You can reveal bottlenecks, graphic confusions, and the most popular pages and features. With little tweaks and informed data analysis, you can slowly streamline and refine your website for the optimum conversion funnel and on-site customer experience.

Minimizing & Defining Your Cybersecurity Risks

Data mining and analysis can help you identify every scrap of sensitive data held by your company. Analytics tools can comb through structured data like your CRM records and unstructured data like emails and chat logs to flag every single sensitive datum. This gives you the ability to delete unnecessary sensitive data and then contain and defend all sensitive data that the company controls.

Refining Your Internal Efficiency

Finally, your own internal processes can be improved by data analytics. This is applied and automated ergonomics. Predictive analytics can prevent items from going out of stock. You can identify bottlenecks and instances of duplicate data. You can also analyze your own speed and efficiency to determine the most effective methods to improve your internal workflow one data-driven step at a time.

Seven Types of Data Analytics Methods

1)  Descriptive Analysis

Descriptive analysis is where it all starts. This asks and answers the question “What happened?”. Descriptive analytics is often where data is collected, tracked, and mined to determine what’s going on under the surface of your business. Getting started with descriptive analysis is like lifting the hood of your business and taking a look at how it all runs. Are sales up? Are views down? Did you get your expected holiday revenue boost? Are visitors reading the blog? Descriptive analysis often reveals status answers and yes or no details.

2) Diagnostic Analysis

Diagnostic analysis helps you determine “Why did this happen?” It is often used to track down the source of technical problems, but diagnostic data can explore the why of any detail. You might you diagnostic analysis to determine why there’s a sales drop in August or why customers suddenly stopped making online appointments. Your diagnostics may find that the calendar widget is broken or the click-box is off-center.

3) Predictive Analysis

Predictive analysis uses trend building and pattern matching to determine “What will happen in the future?” Businesses often use predictive analysis to prepare for sales patterns throughout the year and build expectations for each quarter. However, predictive analysis can also be used to determine which blog topics will be most popular, which colors will be popular by season, right down to when a specific customer will come back for their fifth pack of wool socks.

4) Prescriptive Analysis

Prescriptive analysis uses a combination of diagnostic and predictive analysis to determine “What should be done?” Prescriptive analysis is where you start to use your data to determine the best decision instead of simply taking data into your decision-making process. Prescriptive analysis might compare the predictive results of two decisions and mathematically determine which is more likely to succeed based on previous trends.

5) Cluster, Cohort, and Factor Analysis

Cluster, Cohort, and Factor analysis are all ways to break your data down into smaller groups and find more detailed patterns or insights. Cluster analysis classifies objects in relative groups or data clusters. You might, for example, determine why one area is associated with a specific type of insurance claim. Cohort analysis breaks data sets into groups that share characteristics. Factor analysis breaks a massive data set into smaller sets using a statistical method.

6) Regression Analysis

Regression analysis is interesting. It answers the question “If I change x, how will it affect y and z?” Regression analysis studies the relationship between variables and how changing one variable might affect others in the same system. This might analyze how social media impacts blog readership or how shipping options influence order size.

7) Sentiment Analysis

Then there’s sentiment analysis which transitions from quantitative (numeric) data to qualitative (relative) data. Sentiment analysis uses text analysis and computational linguistics to determine the meaning of statements and the overall feelings around certain concepts. As a very simple example, an algorithm could determine how often a product’s name was associated with the word “satisfied” and its synonyms.

Is Your IT Company Helping You Understand and Acton Data Analytics?

Every business can reap a treasure trove of insights from the data that flows through their website and servers every day. You can collect specific data that provides value, break it up into logical groups, identify trends, predict behaviors, and plan for the future. Every decision in your business could be backed by data. But is it?

If your IT services company is not enabling your understanding and use of data analytics, they should be. Here at GIASPACE, we care about your business’s ability to hone your techniques and grow powered by your own data. Contact us today to consult with IT specialists who can help you understand your data analytics and build an improved data-driven process for your company.