The Dangers of Data Silos

According to a recent report from the McKinsey Global Institute, the data analytics revolution has started to gain momentum, but most companies are capturing only a fraction of the potential value from data and analytics. One of the biggest obstacles is data silos. Most of a company’s data is generated and stored in different systems throughout the organization, and these systems are separate, isolated programs.

Data Silo Consequences

Data silos are a problem for both simple and complicated analyses.

• When data sources are siloed, it is impossible to conduct a simple enterprise-wide search of content. If you do not know exactly which system has the information you need, or it is stored in multiple systems, you must manually search multiple systems to find the answers you are looking for, wasting valuable time. If you forget to check a key system, or you don’t find every silo with relevant data, you will not have all the information you need to make the best possible decisions.
• Siloed data often contains duplicate information stored in individual siloes. When the same content is stored in disparate and unconnected systems, it’s difficult to know which source to trust. Which version is the correct version? How do you choose? What are the consequences of making the wrong choice?
• You cannot gain a complete view of a situation unless you combine information from different systems to understand all the factors impacting your business. Imagine you are a farmer analyzing the most effective planting schedule for this year’s crops. You need to analyze historical data about crop performance, weather forecasts, historical weather trends, and the use of pesticides and fertilizers, but you also need to analyze market trends. If you choose to make your decision based solely on which crop produced the highest yield last year, you may miss that last year’s top performer’s forecasted global production doubled this year, eroding prices and potential profits. Last year’s second-best performer could be the more profitable choice. Only by generating a complete view of the situation can you make the best decisions.

Breaking down silos

If you want to thrive in the age of big data, breaking down data silos is essential. Effective data analytics requires an advanced analytics platform that can aggregate siloed data, blend it, and analyze all your data. Currently, companies spend a lot of time manually collecting and merging this data. A July poll of 130 executives found that 84 percent of companies use manual processes to collect and enter data for reporting and analysis.

Big data analytics provide the answer. Built to aggregate and analyze data from disparate data sources – internal sources like CTRM, CRM, spreadsheets, IoT sensor data, and ERP as well as external sources like market feeds and weather reports – big data anlytics answers your most important questions by analyzing all available data and providing one clear answer.