Transformation of business analytics from business intelligence
- 1. Transformation of Business Analytics from Business Intelligence The primary benefit you will be getting from having Spend Visibility (the by-product of the data transformation process) is that it will facilitate better decision-making. This applies not just to you, the business leader, but to all the levels of the organization, which is what makes data-driven Spend Analysis such a highly sought-after service these days. The question is, though, how do Business Analysts generate the spend data that changes your business almost overnight? Below, we’ll be exploring the main steps of the Spend Analysis Process to get a better idea of how it’s all done, starting with a small introduction to what Spend Analysis is in the first place. You know, in case you forgot. What is Spend Analysis? Spend Analysis is the name given to several collective steps of gathering and processing the spend data of an organization to identify their spend trends. In most cases, this service is provided with the larger aim of identifying saving opportunities, streamlining operations, and improving the organization’s overall Spend Visibility. In easy-to-understand words, it lets you save money and optimize the efficiency of your business operations.
- 2. What Are the Processes Involved in Spend Analysis? 1. Data Extraction (Identification) Identification, or Data Extraction, is the first step of the data transformation process and involves extracting spent data from both internal sources (departments and such) of the organization and external sources (vendors, suppliers) related to the organization. Typically, this extends to every department, plant, and business unit since Spend Analysis requires real-time, comprehensive spend data, leaving no sources out. Some of the prevalent sources of spend analysis data you’ll find are: ● Enterprise Resource Planning Tools ● The organization’s general financial data ● Purchase orders of goods and services 2. Data Consolidation (Gathering) Data Consolidation is the process of consolidating all of the different data you’ve gathered into one central database. It might sound as simple as normal Data Entry operations, but in reality, Data Consolidation comes with many challenges. You’ll see problems like the following when consolidating your data: ●Data is in different languages, formats, and currencies. ● A particular receipt or piece of spend data might not have all the details. ● Columns may not match across sets of data. These problems are some of the biggest reasons project leads prefer to use “hand coding” in more minor cases, where Data Engineers sort this data into neat datasets. For larger datasets, you’ll want to use ETL tools made for this specific purpose.
- 3. 3. Data Cleansing (Cleaning) Data Cleansing is at once one of the essential steps and one of the most frequently mishandled parts. In practice, it’s removing inaccuracies and redundant or corrupt records from the central database. Professionals in data transformation services find and remove errors in the datasets (transactions, descriptions, etc.) to ensure data quality. There are three main steps of Data Cleansing: ● Identifying and correcting unstructured or jumbled data ● Filling in missing values and fields in datasets, along with removing errors ● Finding, fixing, or deleting irrelevant, corrupted, inaccurate, duplicate, or incorrectly formatted data Like you might have guessed, this step aims to produce a clean database that reflects the entire company spend. 4. Data Clustering (Clustering) Data Clustering refers to a technique used by organizations to identify purchases made from the same supplier or vendor using a different name. For example, a supplier’s name in one receipt might be in acronyms but fully spelled out in another. You might even see the same supplier but with a different location after their name. To prevent datasets from becoming confusing, Data Clustering creates supplier grouping for better supplier management. Data Clustering is a massive help in creating reports and insights later down the line. 5. Data Classification (Categorization) Classification is the technique businesses use to categorize the gathered, cleaned, and clustered data into separate categories of information and identify a consolidated spend.
- 4. Spend data for similar goods and services is grouped into predefined, clear categories to make company spend easier to address and manage at all levels of the organization. If all that was a little complicated, here’s a simple definition of this step: Data Classification allows leaders to gain Spend Visibility and make better sourcing decisions by sorting spend data into clearly defined categories. 6. Data Insights (Analyzation) Once the company spend data is categorized into clear categories through Classification, procurement professionals analyze this central database to identify opportunities to save on expenses and streamline operations. These insights are leveraged in the right places by data transformation services experts to reduce costs, such as reducing procurement costs by having all purchasing happen from preferred suppliers that provide discounts. The last and final step, Data Analysis, generates the spend insights, reports, and KPIs resulting from the Spend Analysis process. 7. Data Refresh Doing it once, though, is never enough. Spend Analysis needs to be a repeated process with fresh data sets to keep identifying saving opportunities. Wipe the data and keep on going with the latest data transformation tools! Conclusion At the end of the day, though, analyzing company spend is far easier said than done. The data transformation process used for Spend Analysis holds excellent potential for identifying savings opportunities, but you need both the latest technology and consulting services to ensure a good job is done. Our opinion, though? The risk management, compliance, and profit maximization benefits of Spend Analysis are definitely worth the pain!
- 5. Our methodologies: Please contact us if you'd like to learn more about in2in global automated data analytics solution, which can provide data - driven analysis from your data and enable it to be used for its intended purposes.