Data is the basis of knowledge; information generates evidence and evidence helps you to make wise decisions on knowing consumers, simplifying procedures, and raising production. Data analysis tools help you to convert data into information from which you may compile the evidence needed to support and make your conclusions.
Companies today can gather more data than was reasonable in the past and do it from several sources at a faster pace. The next generation of corporate intelligence, data warehousing, and data analytics is big data—or evidence-based decision-making—that defines acquiring, organizing, analyzing, and managing the data. It calls for fresh data analysis tools, fresh approaches to data organization, and maybe a team of data scientists to help you interpret the data.
For tactical decisions connected with daily operations, real-time data analysis can be applied. Strategic data analysis can happen in any period and reveals long-term patterns.
Let us examine two themes concerning big data:
- The always rising amount of data
- The closing distance separating data collecting from its analysis
How Can These Trends and Others Affect the Running Operations of Your Company ?
Data Volume is Rising Steadily at an Accelerating Pace
The first trend one should take into account is the increase in the data businesses gather.
Processing power and storage were costly before the Internet came around, and ad hoc searches were unworkable. Applications gathered the least information needed to document a transaction, name a customer, or characterize a good or service. Companies used general ledger, payroll, accounts payable, accounts receivable, client information, product description, and invoices among the data they gathered. The CFO asked IT a query request if he/she wished to access the data.
Companies gathered more data and part of it was pulled into data warehouses as the cost of processing power and storage dropped. Online analytical processing (OLAP) of the data in data warehouses let anyone examine data without asking IT authorization.
These days, we gather transaction information and data from many different sources and in several formats. While unstructured data outside databases has grown at an even higher rate, the volume of structured data in databases has grown with rising transaction volumes. Among the modern data collecting tools are (in no specific sequence):
- Digital files
- Cooperation records
- Sales tracking, prospecting, customer profiles—CRM data
- Customer demographic information
- Data acquired by sensors (such as temperature)
- Records
- Photo files
- Loyalty programs compile consumer preferences data.
- Mobile apps offer location data along with search results.
- Social Information
- Geography, GPS, and location information
- Files
- Statistical information taken from open data sources (such as data.gov and data.worldbank.org).
- Text files
- Digital files
- Climate
- Wikis and blogs.
Data comes from many sources: papers, spreadsheets, social media, organized sources including relational databases. Sensors gather data—such as minute warehouse temperatures—and videos—such as tracking who entered and left the reception area. When consumers utilize web applications, companies can gather session data. Examining user session data exposes additional items the consumer looked at besides a bought one. Keeping this data offers a comprehensive narrative of a client’s contacts with the business.
Many businesses have traditionally gathered enormous volumes of data; the financial and insurance sectors are two others. Although vast archives of historical data have existed for some time, these are not huge data; rather, they become massive depending on the analysis you wish to undertake. Big data possess certain qualities including:
- Volume—quantity or size
- Diversity: several forms and systems of construction
- Velocity—speed of data accumulation—variance and velocity are fresh elements that set big data apart from historical data warehouses. Of course, size counts and data volume is rising quicker than ever, thereby taxing storage space and processing capability.
Conventions of This Trend
The increasing trend in data volume puts operational needs on businesses who want to keep and evaluate their data.
- The volume of data will compel businesses to increase their storage capacity and think about criteria for selecting what data to remain online, what data to trash, and what data to forward to offline storage.
- The range of the gathered data challenges the storage solution design. Because of the volume and distribution of unstructured data across several databases and file systems, storage management finds difficulty.
- Organizing the data to maximize it for data analysis is a difficult task when time is limited for intricate extract, convert, and load operations needed to fill data warehouses. The original data will be used more and more for the tactical data analysis.
- Companies have less time to do tactical data analysis since the speed of data acquisition indicates Ignoring all the data could cause missed sales prospects or inability to identify where process improvement would be sensible.
- The quality of the data determines how valuable evidence derived from data analysis is. A correct understanding of the data depends on data accuracy and validity. In both real-time and strategic analysis, keeping the data valid upon collection will help to simplify the data analysis. Data validation is under control of business rules. Applications, policy and procedure manuals, and ad hoc judgments managers make throughout daily operations help to disseminate business rules. Distribution of business rules throughout several applications complicates and rigid data validation. Although they are easy to update, business rules found in policy and process documentation are challenging to enforce. Business rule updates will provide companies who extract the business rules from applications into a rules engine better rapid response capability. They can allocate the suitable spheres of responsibility for business regulations.
- Applications will have to grow to gather and analyze information including personal consumer preferences in order to capture the data required for real-time up-sell and cross-sell possibilities.
- Businesses will have to commit more resources toward data management. Ensuring continuous operation and availability of data in databases and file systems will be responsibility of data managers.
- If businesses want to stop data theft or data compromise, data access and authorization needs will grow increasingly critical.
- Privacy rules complicate data handling. Businesses must decide whether to delete identity from the data, therefore losing the capacity to profile individual consumers, or keep the identity data and run the danger of violating privacy rules.
The Closing Distance Between Data Collecting and Analysis
Another trend is the closing difference between data collecting and analysis.
Evidence comes from data; data is not evidence. Data needs analysis to turn into evidence on which businesses should base choices.
These days, knowing what happened and why it happened is insufficient. Organizations must be aware of current events, future developments, and what steps should be taken to attain the best outcomes.
Reference: “Big Data, Analytics and the Path From Insights to Value,” Steve LaValle, Eric Lesser, Rebecca Shockley, Michael S. Hopkins, Nina Kruschwitz, MITSloan Management Review, Winter 2011, Vol. 52, no. 2.
You will say “no” 99.9 percent of the time. Imagine now, though, if the business could automatically review all of my past transactions and identify other products I bought when I visited to purchase a pair of pants — and then offer me 50 percent off a like purchase? Now that would be pertinent to me. The store is presenting me something I most likely like at a reasonable price instead of another boring credit card offer.
Source: Minelli, Michael; Chamber, Michele; Dhiraj, Ambiga (2012-12-27). Emerging Business Intelligence and Analytic Trends for Today’s Businesses (Wiley CIO) Big Data, Big Analytics Kindle Locations 571-574 Wiley; Kindle Edition.
Data analysis used to be essentially a review of history. Business deals took place and data analysis took place some later. Relational databases let applications save transaction data in real-time. Later on, data extract programs looked over, cleaned, rearranged, and stored the transaction data in a data warehouse. The data analysis came about only then. Data residence and when the analysis takes place define two of the distinctions between data warehousing and big data. To extract, cleanse and transform the data so that it is available for analysis, data warehouses demand time-consuming activities. Companies can do data analysis right at the moment a transaction takes place today.
Originally a back-office role, data analysis In the corporate environment of today, the analysis takes place in daily activities (tactical analysis) and post-event what-if study (strategic analysis).
As the distance separating the corporate transaction from the data analysis grows, the value of the data decreases. Once the data analysis takes place following a transaction, there are fewer chances for activities connected to or motivated by one. A shirt buyer might think of a tie at the moment of purchase, but less likely a week or two later.
Business activity is accelerating, and post-event data analysis comes too late to seize chances for creating new business from transactions. Companies today have to be able to evaluate data at or close to the time of acquisition, ascertain its value, and start corresponding activities to seize commercial prospects connected to transactions.
Consequences of This Movement
The industry and type of the goods and services businesses offer will determine the consequences of this trend.
- Real-time data analysis is essential for consumer product companies so that a website may recommend items a customer views. Point-of-sale systems give consumers buying goods in a store a comparable service by suggesting related products and giving discounts or a chance for sales associates to speak with the customer.
- Products with a long lead time until a sale do not need real-time data analysis to suggest comparable products; nevertheless, data analysis in real-time can pin-point bottlenecks in manufacturing and distribution operations. Businesses marketing this kind of product might also compile industry and competition data and examine it to make sure their products meet the needs of possible consumers.
- Unless you are convinced you have enough data, the data is accurate and a reliable indicator of behavior and preferences, then basing choices on real-time data analysis can be deceptive.
- Data warehouse operators will have to decide what kind of data analysis will remain in the data warehouse and what type will happen in real-time.
- Every business will have to include real-time data analysis into the apps interacting with consumers via a website, a call center, or sales assistant at a point-of-sale terminal.
- Whether the infrastructure can handle the extra processing demand generated by real-time data analysis will rely on an evaluation of its capability.
