Imagine getting lost in a new city without a map or GPS. You’re confused and can’t develop a plan to get you where you want to go. That’s the same thing that happens to a business without updated data mapping software. Before we jump into the topic of mapping your data, let’s delve a little into the topic of data and how you use data to enable better decision making.
Data-driven decision making
According to Northeastern University, data-driven decision making (DDDM for short), is the goal for any business as using data to inform decisions leads to much higher performance than using intuition or making assumptions. Yet, according to the same source, a recent survey found more than half of all businesses said they make decisions without the benefit of data analysis at least half the time. Think of all the money these businesses didn’t make because they made the wrong decision.
Even companies using data to make better decisions, often fail to make the “right” decisions for a variety of reasons from not doing the correct analysis to not collecting the right data to letting data paralyze their decision-making. In fact, a recent study by Inc found up to 73% of data collected by organizations goes unused, thus increasing costs to collect and store the data without improving decision-making. Thinking of data this way, data represents a cost rather than a benefit to the firm.
Big data, the new buzz word in the analytics industry, further compounds efforts to glean insights from data.
Big data
Big data refers to the immensity of data produced through a combination of the web (clickstream, search), IoT (the internet of things), and various internal databases maintained by the organization, such as CRM systems. Big data reflects the 4 Vs, making it harder to analyze. These are:
- Velocity — the pace of data collection is so massive, organizations have trouble analyzing data in real-time. Check out the image below showing how NASCAR uses real-time data to optimize their TV feed using data from social media and other sources.
- Volume — the volume of data collected boggles the mind. Current estimates of the volume of data are 40 zettabytes, a growth of 300X over 2005. We collect about 2.5 quintillion bytes of data every day. [source] Storying a single zettabyte would fill enough data centers to cover 1/5 of the city of Manhatten at an estimated cost of over $200 billion.
- Variety — IBM estimates that 80% of the data collected is unstructured; ie. words, pictures, video, etc. Analyzing this data requires tools not currently available or not effective enough to guide decision-making. For instance, NLP (natural language processors) and ML (machine learning) only determine the sentiment of words from sources like social media posts and they don’t even do that with 100% accuracy.
- Veracity — this V represents the extent to which data are trustworthy, accurate, and reliable.
How to use big data
Thus, big data reflect both massive volume and high complexity — consisting of both structured and unstructured data across disparate databases; making it challenging to analyze. And, business analysts required to analyze big data are in huge demand, while the tools needed are expensive. Data analysis isn’t something you can pick up in an afternoon.
Big data done right allows you to understand customers’ wants and preferences and helps build relationships with them by offering the right solution to their problems. From an operational perspective, data guide production decisions and transportation options to ensure a firm satisfies customer demands for products at the right place at the right time, while reducing costs. IoT solutions help monitor equipment to ensure proper maintenance and monitor performance to ensure the equipment runs at optimal levels.
In marketing, for instance, firms like Netflix and Amazon use big data to make recommendations based on previous customer actions. This analysis allows recommendations and messaging to focus on the particular needs of the individual customer, making customers more likely to purchase and become loyal to the company, which brings more profits to the firm. Other companies use big data to understand how messaging resonates with their target audience to optimize their communication and drive purchase. Finally, big data offers insights that allow firms to match messaging with the customer’s stage in the buying process.
What is data mapping?
Data mapping is the process of linking data from one information system of the business to data from another information system; meaning this type of data analysis requires combining data from various relational databases using a key to accurately connect the data elements. Commonly, tools like Python and SQL help combine data then draw insights from the combined datasets.
To connect different datasets, you need a predefined set of methods and that, in essence, is what data mapping contributes. With effective data mapping, managers and other stakeholders access vast amounts of actionable information in a format that’s easy to understand and less confusing than drawing insights from isolated databases. For instance, if I want to understand how price affects purchase volume, I must first combine results from all my different locations into a single database rather than try to glean something from individual databases for each location containing vast amounts of information in addition to price and sales volume.
According to Wikipedia, data mapping is:
data mapping is the process of creating data element mappings between two distinct data models. Mapping is used as a first step for a wide variety of dataintegration tasks including: Data transformation or data mediation between a data source and a destination.
If that sounds a little confusing or, if you’re more visual, here’s an example of data mapping.
Benefits of data mapping
Data mapping is not only a good means to keep updated records but has the following advantages:
- Get all the company’s information in an organized manner
- With easy access to information, it results in faster decision-making
- Increased efficiency in all business operations
- Through improved customer relations, it increases sales and overall profit
- It gives the managers and workers the ability to identify any emerging trends and act on them
Data mapping techniques
Businesses use different techniques to transform data from different information systems in an organization to one visual representation. These techniques include:
- Data-driven mapping — which involves evaluating data from two different sources simultaneously using heuristics and statistics. The analysis is done to discover complex mapping between the two data sets. It is the most common technique since it automatically finds transformations between the two data types.
- Transformation logic – this technique is used to create applications responsible for data mapping.
- Semantic – Semantic mapping is similar to the auto-connect feature that is used in graphics mapping. The only difference is that the metadata registry can’t be used to look up for data element synonyms. Semantic can only discover exact matches between data columns.
- Hand-coded – this involves data mapping while using graphical mapping tools, procedural code, or by creating XSLT transforms.
Data mapping software
The best software solutions provide visual representations of your data for easy interpretation that makes decision-making better. This software is great for things like data migration and transforming data as well as integrating data across different databases.
IBM InfoSphere DataStage
IBM® InfoSphere® DataStage® provides solutions for businesses from enterprise to smaller businesses who want to power of data solutions in the cloud. The software even integrates with the IBM Watson supercomputer and its vast array of data. The software allows for massive parallel processing to speed analysis and is highly scalable and integrates easily.
Dell Boomi
Dell Boomi is an end-to-end data tool, as you can see above. Boomi offers both integration of data and discovery of new data while managing APIs and data quality. Boomi also offers training to get the most from their software.
Informatica PowerCenter
As with the other software, PowerCenter is scalable to serve your firm as it grows. It offers graphical tools that speed analysis without coding and specializes in rapid prototyping and validation. PowerCenter also offers real-time data analysis capabilities.
Other mapping software
Other data mapping software options exist on the internet and might work very effectively in your data mapping activity. Before choosing a specific software, ensure that it can do the following:
- Offers standard data visualization features
- Should be able to perform data analysis which helps managers make informed decisions
- Easy to use. It should require less or no training
- It must be linked with the cloud for easy retrieval of data
- It must be capable of handling large data for big enterprises
- Ability to generate reports quickly. The reports must be clearly outlined in easy-to-follow guidelines
Data mapping is also the only working alternative for lost records and mismanaged records. Mapping helps a company generate updated reports that help in running the business. Use the best mapping software from the internet to effectively map your data and increase your profit.
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