A vendor cannot price data analytics services until you hand over seven inputs, and each one is easier to prepare before the first call. Selection guides spend their pages on how to judge a vendor. The post covers the buyer's side of the table. You will see what to prepare, why the vendor asks for each item, and what happens to the quote when an item is missing. A short definition comes first, then the pack of seven inputs.
What do data analytics services include?
Data analytics services connect your records, clean them, agree on metric definitions, and present them in dashboards and reports. The vendor delivers the pipeline and the reports. You provide the sources, the definitions, and the access. The quote depends on how ready those inputs are.
Three deliverables sit inside most engagements. The pipeline moves data from each source into one place on a schedule. The definitions fix what each metric means and who owns it. Dashboards and reports then show the metrics to the people who act on them.
Providers use many labels for this work. Data analytics companies, business intelligence services, and plain analytics services all describe versions of it, and the labels say little about scope. Judge an offer by the three deliverables and by who supplies each one.
A scope should also say what happens after delivery. Ask whether the quote covers changes to a report, a new data source, or a fix when a source system changes its format. Those are the requests that arrive in month two.
At Burhani, the work sits under data analysis and reporting. The engagement begins with the sources and ends with a report that someone trusts enough to act on.
What does a data analytics vendor need from you first?
A vendor needs seven things. They are a data source audit and metric definitions, plus a reporting cadence and a dashboard specification. The list ends with a cleaning plan, an access list, and a refresh schedule. With these in hand, a vendor can scope and quote. Without them, the quote carries a guess.
Swipe or use the arrow keys to compare all 3 columns.
Item | What to prepare | Why the vendor asks |
|---|---|---|
Item: Data source audit | What to prepare: List every system and file that holds the data | Why the vendor asks: Sets the number of connections to build |
Item: Metric definitions | What to prepare: One written definition per metric and its owner | Why the vendor asks: Prevents two departments reading one number two ways |
Item: Reporting cadence | What to prepare: Who gets which report and how often | Why the vendor asks: Sets the refresh load |
Item: Dashboard specification | What to prepare: Audience; decisions; filters; drill-down | Why the vendor asks: Turns a request into a build list |
Item: Data cleaning plan | What to prepare: Known duplicates; gaps; formats | Why the vendor asks: Sizes the preparation work |
Item: Access control | What to prepare: Roles and who sees which data | Why the vendor asks: Sets permissions and vendor access |
Item: Refresh schedule | What to prepare: How current each report must be | Why the vendor asks: Decides batch or near-live loads |
Start with two items: the source audit and the metric definitions. The rest follow from them. The audit tells the vendor how many connections to build, and the definitions say what to calculate once the data arrives.
Name one single source of truth for each metric.
Two departments often define the same number differently, and sales may count an order when it is placed while finance counts it when it is invoiced. The vendor cannot choose between them. Someone in your business must, and the choice goes into the definition in writing. Do it before the first call.
The reporting cadence and the refresh schedule are related but separate. Cadence is who receives a report and how often. Refresh is how current the numbers inside the report must be, and a weekly report can still need daily data. Mixing the two up can distort a quote.
Assign an owner on your side for each item. IT usually owns the source audit, finance or operations owns the metric definitions, and a department head owns the dashboard specification. A named owner turns a list into a plan, and a plan without owners is a wish.
Some vendors offer a paid discovery phase to collect these items for you. That is a fair offer, and the pack shortens it. A buyer who arrives with the source audit and the definitions pays for less discovery and gets a firmer quote at the end of it.
Why does data readiness change the quote?
Anaconda's 2020 State of Data Science survey found that respondents spend about 45% of their time getting data ready. That means loading and cleansing it before models or visualizations can be built. A vendor prices that preparation. Clean, well-defined data with easy access shortens it.
The figure is a benchmark for effort. The survey covers data professionals in general and dates from 2020, so it says nothing about your records. Treat it as a reason to take preparation seriously. Preparation is the largest unknown in most quotes. A vendor who has not seen the sources must guess at duplicates, missing fields, and inconsistent formats, and a careful vendor guesses high. Hand over the source audit and a note on known gaps, and the guess becomes an estimate.
Take an illustrative case. A weekly report takes one analyst 3 hours to assemble by hand, which is 150 hours over a 50-week year. A pipeline that refreshes the same report each morning removes most of that effort, but only once the sources are connected and the definitions are agreed. Readiness decides how soon that happens.
The post on the data analysis lifecycle covers the stages of the work in full. Here the subject is commissioning, and quotes for data analytics services stand or fall on the inputs above.
How do you write a dashboard specification for data analytics services?
A dashboard specification names the audience and the decision each chart supports. Added to that are the metric definitions and filters, the drill-down path, and the refresh need. Write one page per dashboard. A vendor can quote against a page like that and test against it at acceptance.
Five fields, in this order, make a usable page.
- Audience: who opens the dashboard, for example, the warehouse supervisor.
- Decision: what that person decides from it, for example, which orders to expedite today.
- Metrics: the defined measures on the page, each pointing to its written definition.
- Filters and drill-down: how the viewer narrows the data, and what lies one click below each number.
- Refresh: how current each chart must be.
One illustrative row shows the format. Audience: operations manager. Decision: whether to add a second shift next week. Metric: orders awaiting dispatch, defined as orders confirmed and not yet dispatched. Refresh: daily by 7 a.m.
A page like this turns a vague request for a dashboard into a build list. The page also becomes the acceptance test. The vendor delivers against it, and you check the result against it.
Keep each field short. A specification of ten pages for one dashboard usually means the audience is not yet clear.
Who should have access to the data in data analytics services?
Name the roles that may see each data set and give the vendor the minimum access it needs. Under section 8(5) of the DPDP Act 2023, a business must protect personal data in its control, including data processed on its behalf. Put vendor access rules in the contract.
The Act, as published by MeitY in the DPDP Act 2023, asks the business to protect personal data in its possession or under its control. The means is reasonable security safeguards that prevent a personal data breach. The duty covers processing done on its behalf by a data processor, and an analytics vendor falls in that group when it handles personal data.
An access list answers the practical side. The list names each role, the data that role may see, and the people in it. The vendor's own staff appear too, with the level of access each needs and a date when that access ends. The post on outsourcing contracts covers the data processing clause in more depth.
Commencement and the rules under the Act are developing matters, so check the current status with your lawyer.
This section is general information and gives no legal advice. A qualified lawyer should review the access and data terms before you sign.
Where does Burhani Technologies fit when you commission data analytics services?
Burhani Technologies connects existing records into dashboards with agreed metric definitions. Reporting is part of our ERP practice, where the work owns metric definitions and traceable operational analysis. Our delivery begins with discovery of your sources, then scope and quote.
→ Describe where your numbers live and who needs to trust them, and book a free consultation.
FAQs on data analytics services
What are data analytics services?
They connect your records, clean them, define the metrics, and present the results in dashboards and reports. The three deliverables are a pipeline, agreed definitions, and the dashboards themselves. A good scope names each one and says who supplies what, so the vendor's quote matches the work you expect.
What should I prepare before hiring a data analytics company?
Prepare the seven-item hand-over pack: a source audit, metric definitions, a reporting cadence, a dashboard specification, a cleaning plan, an access list, and a refresh schedule. Start with the audit and the definitions, because those two shape every other part of the quote. Give them a week. A rushed pack produces a rushed quote when you ask data analytics companies to bid.
What is a single source of truth?
The single source of truth is the one system or sheet that everyone agrees is correct when two numbers disagree. For each metric, you name one, and the definition says where the number comes from. The vendor then builds reports from that source. The argument about which figure is right stops.
How often should a dashboard refresh?
The need for fresh numbers sets the refresh. A decision made once a week rarely needs data from the last hour, while a dispatch screen used all day may need new data every few minutes. Write the refresh need in the specification for each chart. Near-live loads cost more to build and run than daily ones. Ask for the daily option first.
What is a data source audit?
A data source audit is a list of every system and file that holds the data, with an owner for each, and the list includes spreadsheets kept on a laptop. The vendor reads it to count the connections to build, so a forgotten source becomes a change request later.
What does a dashboard specification include?
A specification names the audience, the decision each chart supports, the metric definitions, the filters, the drill-down path, and the refresh need. One page per dashboard is enough. The vendor quotes against that page and tests the finished dashboard against it, so keep each field short and concrete.
What will every vendor quote against?
Prepare the seven inputs, and every quote you receive will price the same work. Lay the quotes side by side after that.
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