Data is is not only your most valuable asset, it’s pervasive.
But how you churn data into actionable business insights is what will help you grow your revenue, increase your customer base, and cut costs.
So, how do you do it? Should you hire full-time expert data analysts or should you outsource it to professionals? Or will self-service business intelligence (BI) tools suffice?
Many small businesses lack employees with the skills and confidence to lead BI projects. They are also wary of outsourcing data analytics because of the high costs and potential data security issues.
Self-service BI solutions provide enough tools for beginners to get up and running with BI. Outsourcing BI is not recommended for small businesses until they develop an analytics foundation or start taking on more complex projects.
This article will provide you with insights into which scenarios favor the use of self-service BI and when you are better off outsourcing the work or hiring your own data analyst.
Comparing your options: Outsource, in-house experts, and self-service BI
Below we’ve got a snapshot of three possible BI options: outsourcing the work, using in-house data scientists, and empowering each of your employees to be a citizen data scientist using self-service BI tools..
Find out how each option works and the benefits and challenges of each.
Involves working with a typical management consultancy firm or a specialist data outsourcing firm to derive actionable businesses insights. The third-party firm will use your business data as well as collect other secondary market data, analyze the information, present the results, and suggest suitable actions.
Professionally trained data experts (qualified in statistics, coding, etc.) hired as full-time staff uses your business data and other publicly available market data to continuously provide you with business insights. In-house data scientists will be a part of your payroll.
These are software tools for non-seasoned beginners to try their hand at analyzing data. In-built formulas and drag-and-drop functions help beginners arrive at data analysis results that form the basis of business decisions. Self-service BI tools are easy-to-use and allow even employees without expert data skills to compare values and arrive at business/market conclusions.
Outsourcing BI vs. self-service BI: When to do what
Here we discuss different scenarios that you may come across and what option–outsourcing BI vs self-service BI–works best.
Less complex analysis:
Some data analysis is less complex and does not require many calculations. For example, if you want to identify which are the highest selling products in your different geographic segments along with the names of the respective sales managers, self-service tools like Tableau or Sisense can help. Self-service BI tools allow you to drag and drop the metrics you want to compare and create graphs that tell you the patterns, highest volume, value, etc.
Strategic decision support:
If your company is creating a new operational strategy or working on marketing campaigns that require large investments, you should work with a third party data consulting firm to provide accurate insights.
The external consultants will also be able to provide you with actionable recommendations. Plus this is going to be a one-time exercise, making it only a one-time cost.
Making predictions or forecasting metrics:
If you are looking at making sales forecast charts or predicting revenue growth, you may have to reach out to external consultants. Predictive analytics and business optimization require advanced analysis of data using various statistical tools and algorithms. Considering your limited expertise and bandwidth, it is recommended that you consult with an expert third-party.
While consulting with third parties is helpful for large and complex assignments, your simple everyday BI needs –tracking revenue, sales rep performance, daily costs, production volume– can easily be done using self-service BI tools, at a fraction of the cost.
Here are 15 reasons why investing in self-service BI will help small business be more productive.
If you plan to take the self-service route
Self-service BI tools offer you features for data preparation, interactive data visualizations, dashboard management, reporting, third-party integrations, and collaboration.
If you’re clear of the scenario that you are in and plan to use self-service BI tools for your data analytics journey, here are five BI software that you must consider (based on top players in our Category Leader Ranking for Q1 2019).
GetApp BI Leaderboard (Category Leaders)
- Klipfolio: Klipfolio is a cloud-based data analytics app for creating and sharing dashboards and reports. User reviews on our platform indicate the tool to be easily customizable, flexible, offering several data connectors, and with all major BI features such as data visualization, query, and report writers.
- Grow: Grow is a self-service BI tool offering smart report builders, data cleaning and preparation toolkit, and visualization library. User reviews on GetApp.com mention the software as easy-to-use, supporting integrations, and offering regular updates.
- Zoho Analytics: Zoho Analytics is a self-service BI software supporting data visualizations, dashboards, reporting, and collaboration. User reviews highly recommend the product’s report builder feature and easy-to-use interface.
- Tableau Software: Tableau offers both cloud-based and on-premises version of its self-service BI tool. The software helps in data exploration, data visualizations, and interactive dashboard creation. An analysis of user reviews on GetApp.com reveals that users love the product’s data visualization, data integration, dashboards, and automated report features.
- Microsoft Power BI: Power BI is a business analytics solution that can connect to multiple data sources, create dashboards and reports, and share or embed insights into your website or app. User reviews on our platform mention its learning curve to be low and appreciate its data collaboration features.
Dashboard feature in Klipfolio
Additional tips to help your small business build a strong foundation for self-service BI:
- Align self-service BI initiatives with organizational goals: Communicate with employees what value your business wants to derive from self-service BI. You can link self-service BI success with desired business outcomes like improved conversion rates or reduced operational expenses.
- Measure the success of different use cases: Track how the use of self-service analytics in different departments has helped achieve business goals. You should compare results over a period of time and between different projects to understand how self-service BI helps.
- Involve business users in designing, developing, and supporting self-service BI: The users of self-service BI tools will be non-IT employees involved in different functions such as marketing, HR, or operations. Encourage them to use the self-service BI tools to track and measure the performance of their units rather than make gut decisions.
- Train business users in self-service BI: Seek sessions with the vendors or use the video or text documents provided by them to help your employees get onboard with self-service BI tools.
- Build flexible data governance models: Flexible data governance policies will encourage different segments of employees to try out self-service BI tools while at the same time cautioning them on the need for data integrity and confidentiality.
Conduct data hacks and contests within your organization to identify your in-house data wizards. They can support your BI initiatives as well as act as your first in-house BI team.
If you plan to go the outsourcing BI way
You must also check out other local data analytics outsourcing players who can meet your specific needs and budget. Another option would be to consider offshoring data analytics to other locations such as India.
Things you must take care of when outsourcing BI:
- Choice of vendor: Partner with data analytics outsourcing vendors who are reputed and cater to your industry. Also check if they have experience of working with small businesses like yours.
- Foolproof contract: A poorly-worded contract that does not stress on data security, confidentiality, deliverable timelines, and required output will not yield the best results. On the contrary, it can undermine your reputation, affect revenues, and other business operations.
- Knowledge transfer: Factor in the price of active knowledge transfer when signing up with data analytics outsourcing vendors. Temporarily engage some of your staff to work with the vendor during the implementation phase to help them better understand the processes and results.
- Effective communication: Communicate with the vendor clearly on your business requirements and the kind of data you have. Collaborate daily to ensure that the project is going on the required track. You must appoint points-of-contact from your organization who will be engaging and communicating with the vendor representatives.
Outsourcing your BI tasks to citizen data scientists or through platforms like Kaggle where budding data scientists bid for work and solve challenges is another option.
Is outsourcing BI losing its sheen?
No, outsourcing BI is definitely not losing its sheen!
The increasing complexity of business challenges and growing volume and velocity of data are just a few of the factors driving the growth of the outsourcing data analytics market. The need for advanced qualitative and quantitative analytics to support accurate predictive and perspective analytics demands the skills of advanced data professionals and specialist firms.
Data analytics market is growing at a CAGR of 22.8 percent 2018 – 2025 (Source)
But that also does not mean any decline in the adoption of self-service BI tools. The self-service BI market remains the bigger in size, of the two. The benefits offered by self-service BI–quicker decision-making, lower costs, and easy accessibility–continue to make self-service BI a preferred option.
Self-service BI market is growing at CAGR of 15.5 percent, 2017 – 2025 (Source)
According to Gartner, by the end of 2019, self-service BI users will produce more analysis than data scientists.
Small businesses will benefit more if they invest in a self-service BI tool (suited to their needs) for quick, everyday decision making and metrics tracking, and engage with third-party data and analytics service providers for complex and larger business strategy cases.
Next steps toward being an analytics-driven decision-making business
Here are great ways you can start and progress in your data analytics and BI journey.
- Start with one or two teams: If you’re just starting to adopt self-service BI tools, start implementing and using it first for a few teams. Sales and marketing happen to be departments where businesses use data analytics the most. Measure the success and ease with which these teams have used the BI tools. Based on the results, expand to bring your whole business to data-driven decision making.
- Create data governance structures: Data governance includes the overall management of your data including determining its availability, usability, security, and integrity. Decide on how you plan to collect and store data as well as the various permission levels for employees.
- Shortlist data analytics partners: There will be instances when you feel that you can’t make accurate data analysis to support your business need. Employ the services of data analytics consultants in such situations. Outsource BI to expert organizations to help you drive analysis and suggest businesses actions for your high investment projects or campaigns.
- Set up your own data team: When you feel data analytics is starting to grow on you and you simply can’t live without it, start building your own data analysis teams. You may get this feeling when you have frequent projects that need complex calculations or when your return on investment (ROI) from data-driven decisions is more than the cost of keeping a full-time in-house analyst! Learn why you need to fall in love with a data scientist.
Read on to discover
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Note: The information contained in this article has been obtained from sources believed to be reliable. The applications selected are examples to show a feature in context and are not intended as endorsements or recommendations.